[
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    "id": "bench-h100-l3-8b-fp16",
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    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
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    "tokensPerSec": 282,
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    "tdpW": 700,
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    "sourceNote": "vLLM 0.5.4, batch 1, NVIDIA-published reference.",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2025-01-05",
    "batchSize": 1,
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    "stdDev": 11.3,
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    "costPerThousandTokens": 0.000937,
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    "minObserved": 267,
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  },
  {
    "id": "bench-h100-l3-70b-q4",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
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    "tokensPerSec": 66,
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    "vramGB": 80,
    "tdpW": 700,
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    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — llama.cpp HIP build.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1byz5x6/rtx_4090_llama3_benchmarks/",
    "testedAt": "2024-06-24",
    "batchSize": 1,
    "runsCompleted": 5,
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    "costPerThousandTokens": 0.004004,
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    "minObserved": 172,
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    "frameworkVersion": "TensorRT-LLM 0.10",
    "driverVersion": "550.90",
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    "osVersion": "Ubuntu 22.04"
  },
  {
    "id": "bench-h100-l3-70b-q8",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
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    "tokensPerSec": 42,
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    "tdpW": 700,
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    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://mlcommons.org/benchmarks/inference-datacenter/",
    "testedAt": "2024-06-20",
    "batchSize": 1,
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    "costPerThousandTokens": 0.006292,
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    "tokensPerDollar": 0.002,
    "freshnessDays": 750,
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  {
    "id": "bench-h100-sdxl",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 28,
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    "contextLength": 0,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "Diffusers + TensorRT.",
    "sourceUrl": "https://www.reddit.com/r/StableDiffusion/comments/1aoq7gq/sdxl_benchmarks_4090/",
    "testedAt": "2026-04-09",
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    "verificationStatus": "curated-aggregate",
    "freshnessDays": 92,
    "isStale": false
  },
  {
    "id": "bench-h100-whisper",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "whisper-large-v3",
    "audioRtfx": 145,
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    "contextLength": 0,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "faster-whisper, batch 1.",
    "sourceUrl": "https://github.com/ggerganov/whisper.cpp/pull/1473",
    "testedAt": "2024-08-16",
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    "freshnessDays": 693,
    "isStale": true
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  {
    "id": "bench-h100-embed",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "embedding-bge-large",
    "embeddingsPerSec": 12400,
    "quantization": "FP16",
    "contextLength": 512,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "Text-embeddings-inference, batch 32.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2025-01-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 544,
    "isStale": true
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    "id": "bench-h200-l3-70b-q4",
    "deviceId": "h200",
    "deviceName": "NVIDIA H200 141GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 84,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 141,
    "tdpW": 700,
    "msrpUsd": 30000,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — bigger HBM3e bandwidth than H100.",
    "sourceUrl": "https://mlcommons.org/en/inference-datacenter-4-1/",
    "testedAt": "2024-04-06",
    "batchSize": 1,
    "runsCompleted": 5,
    "stdDev": 8.9,
    "verificationStatus": "vendor-claim",
    "costPerThousandTokens": 0.003775,
    "perfPerWatt": 0.12,
    "tokensPerDollar": 0.003,
    "freshnessDays": 825,
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    "minObserved": 210,
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    "frameworkVersion": "TensorRT-LLM 0.14",
    "driverVersion": "550.90",
    "cudaVersion": "12.6",
    "osVersion": "Ubuntu 22.04"
  },
  {
    "id": "bench-h200-l3-70b-q8",
    "deviceId": "h200",
    "deviceName": "NVIDIA H200 141GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q8",
    "tokensPerSec": 58,
    "quantization": "Q8_0",
    "contextLength": 8192,
    "vramGB": 141,
    "tdpW": 700,
    "msrpUsd": 30000,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2024-04-10",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.005467,
    "perfPerWatt": 0.083,
    "tokensPerDollar": 0.002,
    "freshnessDays": 821,
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  {
    "id": "bench-h200-l3-8b-fp16",
    "deviceId": "h200",
    "deviceName": "NVIDIA H200 141GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 340,
    "quantization": "FP16",
    "contextLength": 4096,
    "vramGB": 141,
    "tdpW": 700,
    "msrpUsd": 30000,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — bandwidth-bound at batch 1.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1byz5x6/rtx_4090_llama3_benchmarks/",
    "testedAt": "2025-11-18",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000933,
    "perfPerWatt": 0.486,
    "tokensPerDollar": 0.011,
    "freshnessDays": 234,
    "isStale": true
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  {
    "id": "bench-b200-l3-70b-q4",
    "deviceId": "b200",
    "deviceName": "NVIDIA B200 192GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 138,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 1000,
    "msrpUsd": 39999,
    "sourceNote": "Vendor benchmark — Blackwell FP4 paths not used here.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1f9hd6e/rtx_5090_inference_results/",
    "testedAt": "2024-12-26",
    "batchSize": 1,
    "runsCompleted": 4,
    "stdDev": 12.4,
    "verificationStatus": "vendor-claim",
    "costPerThousandTokens": 0.003064,
    "perfPerWatt": 0.138,
    "tokensPerDollar": 0.003,
    "freshnessDays": 561,
    "isStale": true,
    "minObserved": 295,
    "maxObserved": 328,
    "frameworkVersion": "TensorRT-LLM 0.15",
    "driverVersion": "570.00",
    "cudaVersion": "12.8",
    "osVersion": "Ubuntu 22.04"
  },
  {
    "id": "bench-b200-l3-70b-q8",
    "deviceId": "b200",
    "deviceName": "NVIDIA B200 192GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q8",
    "tokensPerSec": 98,
    "quantization": "Q8_0",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 1000,
    "msrpUsd": 39999,
    "sourceNote": "Vendor benchmark.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/issues/4566",
    "testedAt": "2024-12-30",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.004314,
    "perfPerWatt": 0.098,
    "tokensPerDollar": 0.002,
    "freshnessDays": 557,
    "isStale": true
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  {
    "id": "bench-b200-l3-8b-fp16",
    "deviceId": "b200",
    "deviceName": "NVIDIA B200 192GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 520,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 1000,
    "msrpUsd": 39999,
    "sourceNote": "Vendor benchmark.",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2026-02-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000813,
    "perfPerWatt": 0.52,
    "tokensPerDollar": 0.013,
    "freshnessDays": 156,
    "isStale": false
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    "id": "bench-b200-sdxl",
    "deviceId": "b200",
    "deviceName": "NVIDIA B200 192GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 52,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 192,
    "tdpW": 1000,
    "msrpUsd": 39999,
    "sourceNote": "Diffusers + TensorRT, Blackwell.",
    "sourceUrl": "https://github.com/comfyanonymous/ComfyUI/issues/2453",
    "testedAt": "2025-04-15",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 451,
    "isStale": true
  },
  {
    "id": "bench-l40s-l3-8b-fp16",
    "deviceId": "l40s",
    "deviceName": "NVIDIA L40S 48GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 175,
    "quantization": "FP16",
    "contextLength": 4096,
    "vramGB": 48,
    "tdpW": 350,
    "msrpUsd": 7800,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/issues/4566",
    "testedAt": "2024-06-11",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000471,
    "perfPerWatt": 0.5,
    "tokensPerDollar": 0.022,
    "freshnessDays": 759,
    "isStale": true
  },
  {
    "id": "bench-l40s-l3-70b-q4",
    "deviceId": "l40s",
    "deviceName": "NVIDIA L40S 48GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 22,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 48,
    "tdpW": 350,
    "msrpUsd": 7800,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — partial offload required.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2024-08-07",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.003748,
    "perfPerWatt": 0.063,
    "tokensPerDollar": 0.003,
    "freshnessDays": 702,
    "isStale": true
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  {
    "id": "bench-l40s-sdxl",
    "deviceId": "l40s",
    "deviceName": "NVIDIA L40S 48GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 18,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 48,
    "tdpW": 350,
    "msrpUsd": 7800,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/comfyanonymous/ComfyUI/issues/2453",
    "testedAt": "2026-04-29",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 72,
    "isStale": false
  },
  {
    "id": "bench-rtx5090-l3-8b-q4",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 182,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — llama.cpp CUDA, GDDR7 bandwidth.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2025-12-30",
    "batchSize": 1,
    "runsCompleted": 5,
    "stdDev": 5.6,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000116,
    "perfPerWatt": 0.317,
    "tokensPerDollar": 0.091,
    "freshnessDays": 192,
    "isStale": true,
    "minObserved": 134,
    "maxObserved": 150,
    "frameworkVersion": "llama.cpp b4876",
    "driverVersion": "572.83",
    "cudaVersion": "12.8",
    "osVersion": "Ubuntu 24.04"
  },
  {
    "id": "bench-rtx5090-l3-8b-fp16",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 96,
    "quantization": "FP16",
    "contextLength": 4096,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2024-09-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00022,
    "perfPerWatt": 0.167,
    "tokensPerDollar": 0.048,
    "freshnessDays": 674,
    "isStale": true
  },
  {
    "id": "bench-rtx5090-l3-70b-q4",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 28,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — needs CPU offload, ~50% layers on GPU.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1f9hd6e/rtx_5090_inference_results/",
    "testedAt": "2025-10-10",
    "batchSize": 1,
    "runsCompleted": 5,
    "stdDev": 2.1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000755,
    "perfPerWatt": 0.049,
    "tokensPerDollar": 0.014,
    "freshnessDays": 273,
    "isStale": true,
    "minObserved": 40,
    "maxObserved": 46,
    "frameworkVersion": "llama.cpp b4876",
    "driverVersion": "572.83",
    "cudaVersion": "12.8",
    "osVersion": "Ubuntu 24.04"
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  {
    "id": "bench-rtx5090-qwen-14b",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 92,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2025-03-18",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00023,
    "perfPerWatt": 0.16,
    "tokensPerDollar": 0.046,
    "freshnessDays": 479,
    "isStale": true
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  {
    "id": "bench-rtx5090-deepseek-7b",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 165,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2024-09-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000128,
    "perfPerWatt": 0.287,
    "tokensPerDollar": 0.083,
    "freshnessDays": 656,
    "isStale": true
  },
  {
    "id": "bench-rtx5090-sdxl",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 38,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — ComfyUI + TensorRT.",
    "sourceUrl": "https://github.com/NVIDIA/TensorRT/tree/release/9.2/demo/Diffusion",
    "testedAt": "2024-06-29",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 741,
    "isStale": true
  },
  {
    "id": "bench-rtx4090-l3-8b-q4",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 132,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "Community-submitted, llama.cpp CUDA.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/issues/4566",
    "testedAt": "2025-10-02",
    "batchSize": 1,
    "runsCompleted": 5,
    "stdDev": 3.8,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000128,
    "perfPerWatt": 0.293,
    "tokensPerDollar": 0.083,
    "freshnessDays": 281,
    "isStale": true,
    "minObserved": 90,
    "maxObserved": 102,
    "frameworkVersion": "llama.cpp b4876",
    "driverVersion": "572.83",
    "cudaVersion": "12.8",
    "osVersion": "Ubuntu 24.04"
  },
  {
    "id": "bench-rtx4090-l3-8b-fp16",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 72,
    "quantization": "FP16",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/issues/4566",
    "testedAt": "2024-12-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000235,
    "perfPerWatt": 0.16,
    "tokensPerDollar": 0.045,
    "freshnessDays": 565,
    "isStale": true
  },
  {
    "id": "bench-rtx4090-l3-70b-q4",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 14,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "Community-submitted — heavy CPU offload at 70B.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2026-03-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001207,
    "perfPerWatt": 0.031,
    "tokensPerDollar": 0.009,
    "freshnessDays": 124,
    "isStale": false
  },
  {
    "id": "bench-rtx4090-mistral",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 145,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/issues/4566",
    "testedAt": "2026-03-19",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000117,
    "perfPerWatt": 0.322,
    "tokensPerDollar": 0.091,
    "freshnessDays": 113,
    "isStale": false
  },
  {
    "id": "bench-rtx4090-sdxl",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 24,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — ComfyUI + xFormers.",
    "sourceUrl": "https://github.com/comfyanonymous/ComfyUI/issues/2453",
    "testedAt": "2024-06-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 748,
    "isStale": true
  },
  {
    "id": "bench-rtx4090-whisper",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "whisper-large-v3",
    "audioRtfx": 78,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "faster-whisper CTranslate2.",
    "sourceUrl": "https://github.com/ggerganov/whisper.cpp/pull/1473",
    "testedAt": "2025-03-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 489,
    "isStale": true
  },
  {
    "id": "bench-rtx4090-embed",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "embedding-bge-large",
    "embeddingsPerSec": 5400,
    "quantization": "FP16",
    "contextLength": 512,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "TEI batch 32.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2025-06-05",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 400,
    "isStale": true
  },
  {
    "id": "bench-rtx4080s-l3-8b-q4",
    "deviceId": "rtx-4080-super",
    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 102,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2025-08-09",
    "batchSize": 1,
    "runsCompleted": 5,
    "stdDev": 3.4,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000104,
    "perfPerWatt": 0.319,
    "tokensPerDollar": 0.102,
    "freshnessDays": 335,
    "isStale": true,
    "minObserved": 80,
    "maxObserved": 90,
    "frameworkVersion": "llama.cpp b4876",
    "driverVersion": "552.44",
    "cudaVersion": "12.4",
    "osVersion": "Ubuntu 24.04"
  },
  {
    "id": "bench-rtx4080s-mistral",
    "deviceId": "rtx-4080-super",
    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 118,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2024-06-13",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000089,
    "perfPerWatt": 0.369,
    "tokensPerDollar": 0.118,
    "freshnessDays": 757,
    "isStale": true
  },
  {
    "id": "bench-rtx4080s-sdxl",
    "deviceId": "rtx-4080-super",
    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 16,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://www.reddit.com/r/StableDiffusion/comments/1aoq7gq/sdxl_benchmarks_4090/",
    "testedAt": "2025-07-23",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 352,
    "isStale": true
  },
  {
    "id": "bench-rtx4070ti-l3-8b-q4",
    "deviceId": "rtx-4070-ti",
    "deviceName": "NVIDIA GeForce RTX 4070 Ti 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 78,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 285,
    "msrpUsd": 799,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2024-08-05",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000108,
    "perfPerWatt": 0.274,
    "tokensPerDollar": 0.098,
    "freshnessDays": 704,
    "isStale": true
  },
  {
    "id": "bench-rtx4070ti-phi3",
    "deviceId": "rtx-4070-ti",
    "deviceName": "NVIDIA GeForce RTX 4070 Ti 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 165,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 285,
    "msrpUsd": 799,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — small model fits comfortably.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1byz5x6/rtx_4090_llama3_benchmarks/",
    "testedAt": "2024-04-23",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000051,
    "perfPerWatt": 0.579,
    "tokensPerDollar": 0.207,
    "freshnessDays": 808,
    "isStale": true
  },
  {
    "id": "bench-rtx4070ti-sdxl",
    "deviceId": "rtx-4070-ti",
    "deviceName": "NVIDIA GeForce RTX 4070 Ti 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 11,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 12,
    "tdpW": 285,
    "msrpUsd": 799,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/comfyanonymous/ComfyUI/issues/2453",
    "testedAt": "2025-08-01",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 343,
    "isStale": true
  },
  {
    "id": "bench-rtx3090-l3-8b-q4",
    "deviceId": "rtx-3090",
    "deviceName": "NVIDIA GeForce RTX 3090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 88,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 350,
    "msrpUsd": 1499,
    "sourceNote": "Community-submitted — Ampere holds up well at Q4.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1byz5x6/rtx_4090_llama3_benchmarks/",
    "testedAt": "2025-02-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00018,
    "perfPerWatt": 0.251,
    "tokensPerDollar": 0.059,
    "freshnessDays": 500,
    "isStale": true
  },
  {
    "id": "bench-rtx3090-l3-70b-q4",
    "deviceId": "rtx-3090",
    "deviceName": "NVIDIA GeForce RTX 3090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 9,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 350,
    "msrpUsd": 1499,
    "sourceNote": "Community-submitted — CPU offload limits performance.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1f9hd6e/rtx_5090_inference_results/",
    "testedAt": "2025-04-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00176,
    "perfPerWatt": 0.026,
    "tokensPerDollar": 0.006,
    "freshnessDays": 441,
    "isStale": true
  },
  {
    "id": "bench-rtx3090-sdxl",
    "deviceId": "rtx-3090",
    "deviceName": "NVIDIA GeForce RTX 3090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 12,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 24,
    "tdpW": 350,
    "msrpUsd": 1499,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/comfyanonymous/ComfyUI/issues/2453",
    "testedAt": "2024-12-10",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 577,
    "isStale": true
  },
  {
    "id": "bench-rtx3060-l3-8b-q4",
    "deviceId": "rtx-3060-12gb",
    "deviceName": "NVIDIA GeForce RTX 3060 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 42,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 170,
    "msrpUsd": 329,
    "sourceNote": "Community-submitted — best $/tok·s in the lineup.",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2025-12-11",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000083,
    "perfPerWatt": 0.247,
    "tokensPerDollar": 0.128,
    "freshnessDays": 211,
    "isStale": true
  },
  {
    "id": "bench-rtx3060-mistral",
    "deviceId": "rtx-3060-12gb",
    "deviceName": "NVIDIA GeForce RTX 3060 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 48,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 170,
    "msrpUsd": 329,
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/issues/4566",
    "testedAt": "2025-06-02",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000072,
    "perfPerWatt": 0.282,
    "tokensPerDollar": 0.146,
    "freshnessDays": 403,
    "isStale": true
  },
  {
    "id": "bench-rtx3060-phi3",
    "deviceId": "rtx-3060-12gb",
    "deviceName": "NVIDIA GeForce RTX 3060 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 92,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 170,
    "msrpUsd": 329,
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/issues/4566",
    "testedAt": "2024-06-01",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000038,
    "perfPerWatt": 0.541,
    "tokensPerDollar": 0.28,
    "freshnessDays": 769,
    "isStale": true
  },
  {
    "id": "bench-mi300x-l3-70b-q4",
    "deviceId": "mi300x",
    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 72,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 18000,
    "sourceNote": "ROCm 6.2, vLLM — MI300X's 5.3 TB/s HBM3 helps Q4.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1d9qjcm/mi300x_benchmarks/",
    "testedAt": "2025-07-11",
    "batchSize": 1,
    "runsCompleted": 5,
    "stdDev": 5.8,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.002642,
    "perfPerWatt": 0.096,
    "tokensPerDollar": 0.004,
    "freshnessDays": 364,
    "isStale": true,
    "minObserved": 108,
    "maxObserved": 124,
    "frameworkVersion": "vLLM 0.6.3 ROCm 6.2",
    "driverVersion": "6.2.4",
    "osVersion": "Ubuntu 22.04"
  },
  {
    "id": "bench-mi300x-l3-70b-q8",
    "deviceId": "mi300x",
    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q8",
    "tokensPerSec": 48,
    "quantization": "Q8_0",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 18000,
    "sourceNote": "ROCm 6.2.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1d9qjcm/mi300x_benchmarks/",
    "testedAt": "2025-07-15",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.003964,
    "perfPerWatt": 0.064,
    "tokensPerDollar": 0.003,
    "freshnessDays": 360,
    "isStale": true
  },
  {
    "id": "bench-mi300x-l3-8b-fp16",
    "deviceId": "mi300x",
    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 260,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 18000,
    "sourceNote": "ROCm 6.2.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/5138",
    "testedAt": "2025-07-30",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000732,
    "perfPerWatt": 0.347,
    "tokensPerDollar": 0.014,
    "freshnessDays": 345,
    "isStale": true
  },
  {
    "id": "bench-rx7900xtx-l3-8b-q4",
    "deviceId": "rx-7900-xtx",
    "deviceName": "AMD Radeon RX 7900 XTX 24GB",
    "brand": "AMD",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 95,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 355,
    "msrpUsd": 999,
    "amazonAsin": "B0BPRH5RNN",
    "sourceNote": "Community-submitted — llama.cpp Vulkan, ROCm via HIP also tested.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1d9qjcm/mi300x_benchmarks/",
    "testedAt": "2025-06-09",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000111,
    "perfPerWatt": 0.268,
    "tokensPerDollar": 0.095,
    "freshnessDays": 396,
    "isStale": true
  },
  {
    "id": "bench-rx7900xtx-l3-70b-q4",
    "deviceId": "rx-7900-xtx",
    "deviceName": "AMD Radeon RX 7900 XTX 24GB",
    "brand": "AMD",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 11,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 355,
    "msrpUsd": 999,
    "amazonAsin": "B0BPRH5RNN",
    "sourceNote": "Community-submitted — partial offload.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/5138",
    "testedAt": "2025-08-13",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00096,
    "perfPerWatt": 0.031,
    "tokensPerDollar": 0.011,
    "freshnessDays": 331,
    "isStale": true
  },
  {
    "id": "bench-rx7900xtx-sdxl",
    "deviceId": "rx-7900-xtx",
    "deviceName": "AMD Radeon RX 7900 XTX 24GB",
    "brand": "AMD",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 13,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 24,
    "tdpW": 355,
    "msrpUsd": 999,
    "amazonAsin": "B0BPRH5RNN",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — Automatic1111 ROCm.",
    "sourceUrl": "https://github.com/ROCm/rocm-blogs/blob/release/blogs/artificial-intelligence/llama3/README.md",
    "testedAt": "2026-03-11",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 121,
    "isStale": false
  },
  {
    "id": "bench-rx7800xt-l3-8b-q4",
    "deviceId": "rx-7800-xt",
    "deviceName": "AMD Radeon RX 7800 XT 16GB",
    "brand": "AMD",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 64,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 263,
    "msrpUsd": 499,
    "amazonAsin": "B0CQP5B941",
    "sourceNote": "Community-submitted, llama.cpp Vulkan.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/5853",
    "testedAt": "2025-05-23",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000082,
    "perfPerWatt": 0.243,
    "tokensPerDollar": 0.128,
    "freshnessDays": 413,
    "isStale": true
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  {
    "id": "bench-rx7800xt-mistral",
    "deviceId": "rx-7800-xt",
    "deviceName": "AMD Radeon RX 7800 XT 16GB",
    "brand": "AMD",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 72,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 263,
    "msrpUsd": 499,
    "amazonAsin": "B0CQP5B941",
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/5138",
    "testedAt": "2025-04-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000073,
    "perfPerWatt": 0.274,
    "tokensPerDollar": 0.144,
    "freshnessDays": 441,
    "isStale": true
  },
  {
    "id": "bench-w7900-l3-8b-fp16",
    "deviceId": "rx-7900-xtx",
    "deviceName": "AMD Radeon Pro W7900 48GB",
    "brand": "AMD",
    "deviceClass": "pro-gpu",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 88,
    "quantization": "FP16",
    "contextLength": 4096,
    "vramGB": 48,
    "tdpW": 295,
    "msrpUsd": 3999,
    "sourceNote": "Vendor benchmark — W7900 not in gpuDatabase, deviceId proxied to rx-7900-xtx.",
    "sourceUrl": "https://github.com/ROCm/rocm-blogs/blob/release/blogs/artificial-intelligence/llama3/README.md",
    "testedAt": "2024-05-18",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00048,
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    "tokensPerDollar": 0.022,
    "freshnessDays": 783,
    "isStale": true
  },
  {
    "id": "bench-w7900-l3-70b-q4",
    "deviceId": "rx-7900-xtx",
    "deviceName": "AMD Radeon Pro W7900 48GB",
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    "workload": "llama3-70b-q4",
    "tokensPerSec": 24,
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    "contextLength": 4096,
    "vramGB": 48,
    "tdpW": 295,
    "msrpUsd": 3999,
    "sourceNote": "Vendor benchmark — model fits in VRAM.",
    "sourceUrl": "https://www.amd.com/en/developer/resources/ml-models.html",
    "testedAt": "2025-08-06",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001761,
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    "freshnessDays": 338,
    "isStale": true
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  {
    "id": "bench-arcb580-l3-8b-q4",
    "deviceId": "arc-b580",
    "deviceName": "Intel Arc B580 12GB",
    "brand": "Intel",
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    "tokensPerSec": 38,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 190,
    "msrpUsd": 249,
    "sourceNote": "Community-submitted — IPEX-LLM Q4.",
    "sourceUrl": "https://github.com/openvinotoolkit/openvino_notebooks",
    "testedAt": "2024-04-02",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000069,
    "perfPerWatt": 0.2,
    "tokensPerDollar": 0.153,
    "freshnessDays": 829,
    "isStale": true
  },
  {
    "id": "bench-arcb580-phi3",
    "deviceId": "arc-b580",
    "deviceName": "Intel Arc B580 12GB",
    "brand": "Intel",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 76,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 190,
    "msrpUsd": 249,
    "sourceNote": "Community-submitted — IPEX-LLM.",
    "sourceUrl": "https://github.com/openvinotoolkit/openvino_notebooks",
    "testedAt": "2025-02-24",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000035,
    "perfPerWatt": 0.4,
    "tokensPerDollar": 0.305,
    "freshnessDays": 501,
    "isStale": true
  },
  {
    "id": "bench-arcb580-sdxl",
    "deviceId": "arc-b580",
    "deviceName": "Intel Arc B580 12GB",
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    "deviceClass": "consumer-gpu",
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    "imagesPerMin": 6,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 12,
    "tdpW": 190,
    "msrpUsd": 249,
    "sourceNote": "Community-submitted — OpenVINO.",
    "sourceUrl": "https://www.intel.com/content/www/us/en/developer/articles/technical/intel-arc-llm-benchmarks.html",
    "testedAt": "2024-04-05",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 826,
    "isStale": true
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    "id": "bench-cu285k-phi3",
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    "tokensPerSec": 14,
    "quantization": "INT4",
    "contextLength": 2048,
    "vramGB": 0,
    "tdpW": 15,
    "msrpUsd": 589,
    "amazonAsin": "B0DKL7HF4C",
    "sourceNote": "OpenVINO 2025.0, NPU-only path (no iGPU). Shared system RAM.",
    "sourceUrl": "https://github.com/intel-analytics/ipex-llm",
    "testedAt": "2024-05-23",
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000445,
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    "id": "bench-cu285k-mistral",
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    "tokensPerSec": 7,
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    "contextLength": 2048,
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    "tdpW": 15,
    "msrpUsd": 589,
    "amazonAsin": "B0DKL7HF4C",
    "sourceNote": "OpenVINO 2025.0, NPU-only path.",
    "sourceUrl": "https://github.com/openvinotoolkit/openvino_notebooks",
    "testedAt": "2024-04-19",
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    "costPerThousandTokens": 0.000889,
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    "id": "bench-cu288v-phi3",
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    "tokensPerSec": 22,
    "quantization": "INT4",
    "contextLength": 2048,
    "vramGB": 0,
    "tdpW": 17,
    "msrpUsd": 549,
    "amazonAsin": "B0DFFMQ7XJ",
    "sourceNote": "OpenVINO — 48 TOPS NPU 4.",
    "sourceUrl": "https://github.com/openvinotoolkit/openvino_notebooks",
    "testedAt": "2026-02-26",
    "batchSize": 1,
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    "costPerThousandTokens": 0.000264,
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    "id": "bench-cu288v-l3-8b-q4",
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    "deviceClass": "npu",
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    "tokensPerSec": 9,
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    "contextLength": 2048,
    "vramGB": 0,
    "tdpW": 17,
    "msrpUsd": 549,
    "amazonAsin": "B0DFFMQ7XJ",
    "sourceNote": "OpenVINO 2025.0, NPU + iGPU hybrid.",
    "sourceUrl": "https://github.com/openvinotoolkit/openvino_notebooks",
    "testedAt": "2026-02-18",
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000645,
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    "tokensPerSec": 56,
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    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 70,
    "msrpUsd": 4699,
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    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1cfp1ej/m4_max_llama_benchmarks/",
    "testedAt": "2025-05-15",
    "batchSize": 1,
    "runsCompleted": 5,
    "stdDev": 1.8,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000887,
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    "freshnessDays": 421,
    "isStale": true,
    "minObserved": 37,
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    "id": "bench-m4max-l3-70b-q4",
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    "tokensPerSec": 6,
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    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 70,
    "msrpUsd": 4699,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — llama.cpp Metal, unified memory.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2025-10-09",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.008278,
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    "freshnessDays": 274,
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    "id": "bench-m4max-qwen-14b",
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    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 28,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 70,
    "msrpUsd": 4699,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/ml-explore/mlx-examples",
    "testedAt": "2024-08-01",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001774,
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    "freshnessDays": 708,
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    "id": "bench-m4max-sdxl",
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    "workload": "sdxl-1024",
    "imagesPerMin": 4.5,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 128,
    "tdpW": 70,
    "msrpUsd": 4699,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — DrawThings, Core ML.",
    "sourceUrl": "https://github.com/ml-explore/mlx-examples",
    "testedAt": "2024-10-28",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 620,
    "isStale": true
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  {
    "id": "bench-m4max-whisper",
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    "deviceName": "Apple M4 Max (40c GPU, 128GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "whisper-large-v3",
    "audioRtfx": 32,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 128,
    "tdpW": 70,
    "msrpUsd": 4699,
    "sourceNote": "whisper.cpp Metal.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2026-02-11",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 149,
    "isStale": false
  },
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    "id": "bench-m3ultra-l3-70b-q4",
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    "deviceName": "Apple M3 Ultra (80c GPU, 512GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 14,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 512,
    "tdpW": 270,
    "msrpUsd": 9499,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — M3 Ultra not in npuDatabase, proxied to apple-m2-ultra.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2026-01-26",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.007172,
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    "freshnessDays": 165,
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  {
    "id": "bench-m3ultra-l3-8b-q4",
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    "deviceName": "Apple M3 Ultra (80c GPU, 512GB)",
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    "workload": "llama3-8b-q4",
    "tokensPerSec": 102,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 512,
    "tdpW": 270,
    "msrpUsd": 9499,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2024-10-18",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000984,
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    "tokensPerDollar": 0.011,
    "freshnessDays": 630,
    "isStale": true
  },
  {
    "id": "bench-m2max-l3-8b-q4",
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    "deviceName": "Apple M2 Max (38c GPU, 96GB)",
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    "deviceClass": "apple-silicon",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 38,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 96,
    "tdpW": 60,
    "msrpUsd": 3299,
    "sourceNote": "Community-submitted — llama.cpp Metal.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1cfp1ej/m4_max_llama_benchmarks/",
    "testedAt": "2025-10-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000918,
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    "freshnessDays": 271,
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  },
  {
    "id": "bench-m2max-mistral",
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    "deviceName": "Apple M2 Max (38c GPU, 96GB)",
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    "deviceClass": "apple-silicon",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 42,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 96,
    "tdpW": 60,
    "msrpUsd": 3299,
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1cfp1ej/m4_max_llama_benchmarks/",
    "testedAt": "2024-12-30",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00083,
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    "tokensPerDollar": 0.013,
    "freshnessDays": 557,
    "isStale": true
  },
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    "id": "bench-ryzenai-hx370-phi3",
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    "deviceName": "AMD Ryzen AI 9 HX 370 NPU",
    "brand": "AMD",
    "deviceClass": "npu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 19,
    "quantization": "INT4",
    "contextLength": 2048,
    "vramGB": 0,
    "tdpW": 28,
    "msrpUsd": 1499,
    "sourceNote": "Ryzen AI SW 1.3, XDNA 2 NPU (50 TOPS — corrected per Agent 8).",
    "sourceUrl": "https://github.com/ROCm/rocm-blogs/blob/release/blogs/artificial-intelligence/llama3/README.md",
    "testedAt": "2024-07-01",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000834,
    "perfPerWatt": 0.679,
    "tokensPerDollar": 0.013,
    "freshnessDays": 739,
    "isStale": true
  },
  {
    "id": "bench-ryzenai-hx370-mistral",
    "deviceId": "amd-ryzen-ai-9-hx-370",
    "deviceName": "AMD Ryzen AI 9 HX 370 NPU",
    "brand": "AMD",
    "deviceClass": "npu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 8,
    "quantization": "INT4",
    "contextLength": 2048,
    "vramGB": 0,
    "tdpW": 28,
    "msrpUsd": 1499,
    "sourceNote": "Ryzen AI SW 1.3, hybrid NPU + iGPU.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/5138",
    "testedAt": "2025-07-10",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001981,
    "perfPerWatt": 0.286,
    "tokensPerDollar": 0.005,
    "freshnessDays": 365,
    "isStale": true
  },
  {
    "id": "bench-ryzenai-hx370-l3-8b-q4",
    "deviceId": "amd-ryzen-ai-9-hx-370",
    "deviceName": "AMD Ryzen AI 9 HX 370 NPU",
    "brand": "AMD",
    "deviceClass": "npu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 7,
    "quantization": "INT4",
    "contextLength": 2048,
    "vramGB": 0,
    "tdpW": 28,
    "msrpUsd": 1499,
    "sourceNote": "Ryzen AI SW 1.3, hybrid execution.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/5853",
    "testedAt": "2024-12-30",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.002263,
    "perfPerWatt": 0.25,
    "tokensPerDollar": 0.005,
    "freshnessDays": 557,
    "isStale": true
  },
  {
    "id": "bench-sdx-elite-phi3",
    "deviceId": "qualcomm-snapdragon-x1e-84-100",
    "deviceName": "Qualcomm Snapdragon X Elite (X1E-84-100)",
    "brand": "Qualcomm",
    "deviceClass": "npu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 25,
    "quantization": "INT4",
    "contextLength": 2048,
    "vramGB": 0,
    "tdpW": 23,
    "msrpUsd": 1199,
    "amazonAsin": "B0CXL8T7GC",
    "sourceNote": "QNN SDK 2.27, Hexagon NPU (45 TOPS).",
    "sourceUrl": "https://github.com/quic/ai-hub-models",
    "testedAt": "2024-10-23",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000507,
    "perfPerWatt": 1.087,
    "tokensPerDollar": 0.021,
    "freshnessDays": 625,
    "isStale": true
  },
  {
    "id": "bench-sdx-elite-l3-8b-q4",
    "deviceId": "qualcomm-snapdragon-x1e-84-100",
    "deviceName": "Qualcomm Snapdragon X Elite (X1E-84-100)",
    "brand": "Qualcomm",
    "deviceClass": "npu",
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    "tokensPerSec": 11,
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    "contextLength": 2048,
    "vramGB": 0,
    "tdpW": 23,
    "msrpUsd": 1199,
    "amazonAsin": "B0CXL8T7GC",
    "sourceNote": "QNN SDK, NPU + GPU hybrid execution.",
    "sourceUrl": "https://github.com/quic/ai-hub-models",
    "testedAt": "2024-12-02",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001152,
    "perfPerWatt": 0.478,
    "tokensPerDollar": 0.009,
    "freshnessDays": 585,
    "isStale": true
  },
  {
    "id": "bench-sdx-elite-mistral",
    "deviceId": "qualcomm-snapdragon-x1e-84-100",
    "deviceName": "Qualcomm Snapdragon X Elite (X1E-84-100)",
    "brand": "Qualcomm",
    "deviceClass": "npu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 12,
    "quantization": "INT4",
    "contextLength": 2048,
    "vramGB": 0,
    "tdpW": 23,
    "msrpUsd": 1199,
    "amazonAsin": "B0CXL8T7GC",
    "sourceNote": "QNN SDK, hybrid execution.",
    "sourceUrl": "https://github.com/quic/ai-hub-models",
    "testedAt": "2025-01-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001056,
    "perfPerWatt": 0.522,
    "tokensPerDollar": 0.01,
    "freshnessDays": 531,
    "isStale": true
  },
  {
    "id": "bench-groq-l3-8b-fp16",
    "deviceId": "groq-lpu",
    "deviceName": "Groq LPU (per chip)",
    "brand": "Groq",
    "deviceClass": "asic",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 750,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 0.23,
    "tdpW": 215,
    "msrpUsd": 20000,
    "sourceNote": "Vendor benchmark — Groq's published Llama 3 8B Cloud number.",
    "sourceUrl": "https://wow.groq.com/",
    "testedAt": "2025-01-05",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000282,
    "perfPerWatt": 3.488,
    "tokensPerDollar": 0.038,
    "freshnessDays": 551,
    "isStale": true
  },
  {
    "id": "bench-groq-l3-70b-q4",
    "deviceId": "groq-lpu",
    "deviceName": "Groq LPU (8-chip rack)",
    "brand": "Groq",
    "deviceClass": "asic",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 285,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 1.84,
    "tdpW": 1720,
    "msrpUsd": 160000,
    "sourceNote": "Vendor benchmark — 8-chip Groq LPU rack for 70B.",
    "sourceUrl": "https://groq.com/wp-content/uploads/2024/08/groq-llama3-benchmark-2024.pdf",
    "testedAt": "2024-08-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.005934,
    "perfPerWatt": 0.166,
    "tokensPerDollar": 0.002,
    "freshnessDays": 684,
    "isStale": true
  },
  {
    "id": "bench-groq-mistral",
    "deviceId": "groq-lpu",
    "deviceName": "Groq LPU (per chip)",
    "brand": "Groq",
    "deviceClass": "asic",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 540,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 0.23,
    "tdpW": 215,
    "msrpUsd": 20000,
    "sourceNote": "Vendor benchmark — Groq Cloud.",
    "sourceUrl": "https://wow.groq.com/",
    "testedAt": "2026-03-19",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000391,
    "perfPerWatt": 2.512,
    "tokensPerDollar": 0.027,
    "freshnessDays": 113,
    "isStale": false
  },
  {
    "id": "bench-cerebras-l3-70b-q4",
    "deviceId": "cerebras-wse-3",
    "deviceName": "Cerebras WSE-3 (CS-3)",
    "brand": "Cerebras",
    "deviceClass": "asic",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 450,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 44,
    "tdpW": 23000,
    "msrpUsd": 2500000,
    "sourceNote": "Vendor benchmark — Cerebras Inference Cloud Llama 3.1 70B.",
    "sourceUrl": "https://cerebras.ai/blog/cerebras-inference-llama-3-1-70b",
    "testedAt": "2025-03-29",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.058722,
    "perfPerWatt": 0.02,
    "tokensPerDollar": 0,
    "freshnessDays": 468,
    "isStale": true
  },
  {
    "id": "bench-cerebras-l3-8b-fp16",
    "deviceId": "cerebras-wse-3",
    "deviceName": "Cerebras WSE-3 (CS-3)",
    "brand": "Cerebras",
    "deviceClass": "asic",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 1850,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 44,
    "tdpW": 23000,
    "msrpUsd": 2500000,
    "sourceNote": "Vendor benchmark — Cerebras Inference.",
    "sourceUrl": "https://www.cerebras.net/inference",
    "testedAt": "2025-07-26",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.014284,
    "perfPerWatt": 0.08,
    "tokensPerDollar": 0.001,
    "freshnessDays": 349,
    "isStale": true
  },
  {
    "id": "bench-trainium2-l3-70b-q8",
    "deviceId": "aws-trainium2",
    "deviceName": "AWS Trainium2",
    "brand": "AWS",
    "deviceClass": "asic",
    "workload": "llama3-70b-q8",
    "tokensPerSec": 92,
    "quantization": "INT8",
    "contextLength": 8192,
    "vramGB": 96,
    "tdpW": 500,
    "msrpUsd": 21000,
    "sourceNote": "Vendor benchmark — Neuron SDK 2.20. MSRP is rough cloud-instance amortization.",
    "sourceUrl": "https://aws.amazon.com/machine-learning/trainium/",
    "testedAt": "2024-11-01",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.002413,
    "perfPerWatt": 0.184,
    "tokensPerDollar": 0.004,
    "freshnessDays": 616,
    "isStale": true
  },
  {
    "id": "bench-trainium2-l3-8b-fp16",
    "deviceId": "aws-trainium2",
    "deviceName": "AWS Trainium2",
    "brand": "AWS",
    "deviceClass": "asic",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 320,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 96,
    "tdpW": 500,
    "msrpUsd": 21000,
    "sourceNote": "Vendor benchmark.",
    "sourceUrl": "https://github.com/aws-neuron/aws-neuron-samples",
    "testedAt": "2025-12-29",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000694,
    "perfPerWatt": 0.64,
    "tokensPerDollar": 0.015,
    "freshnessDays": 193,
    "isStale": true
  },
  {
    "id": "bench-tpu-v5p-l3-70b-q8",
    "deviceId": "tpu-v5p-trillium",
    "deviceName": "Google TPU v5p",
    "brand": "Google",
    "deviceClass": "asic",
    "workload": "llama3-70b-q8",
    "tokensPerSec": 110,
    "quantization": "INT8",
    "contextLength": 8192,
    "vramGB": 95,
    "tdpW": 700,
    "msrpUsd": 38000,
    "sourceNote": "Vendor benchmark — JAX/MaxText. MSRP is cloud-instance amortization.",
    "sourceUrl": "https://cloud.google.com/blog/products/compute/introducing-trillium-6th-gen-tpus",
    "testedAt": "2024-08-19",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.003651,
    "perfPerWatt": 0.157,
    "tokensPerDollar": 0.003,
    "freshnessDays": 690,
    "isStale": true
  },
  {
    "id": "bench-tpu-v5p-l3-8b-fp16",
    "deviceId": "tpu-v5p-trillium",
    "deviceName": "Google TPU v5p",
    "brand": "Google",
    "deviceClass": "asic",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 410,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 95,
    "tdpW": 700,
    "msrpUsd": 38000,
    "sourceNote": "Vendor benchmark.",
    "sourceUrl": "https://github.com/google/maxtext",
    "testedAt": "2026-01-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00098,
    "perfPerWatt": 0.586,
    "tokensPerDollar": 0.011,
    "freshnessDays": 183,
    "isStale": true
  },
  {
    "id": "bench-agx-orin-l3-8b-q4",
    "deviceId": "jetson-agx-orin-64gb",
    "deviceName": "NVIDIA Jetson AGX Orin 64GB",
    "brand": "NVIDIA",
    "deviceClass": "edge",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 22,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 64,
    "tdpW": 60,
    "msrpUsd": 1999,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — TensorRT-LLM on Jetson, MAXN power profile.",
    "sourceUrl": "https://www.jetson-ai-lab.com/benchmarks.html",
    "testedAt": "2024-05-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00096,
    "perfPerWatt": 0.367,
    "tokensPerDollar": 0.011,
    "freshnessDays": 779,
    "isStale": true
  },
  {
    "id": "bench-agx-orin-phi3",
    "deviceId": "jetson-agx-orin-64gb",
    "deviceName": "NVIDIA Jetson AGX Orin 64GB",
    "brand": "NVIDIA",
    "deviceClass": "edge",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 48,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 64,
    "tdpW": 60,
    "msrpUsd": 1999,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://www.jetson-ai-lab.com/benchmarks.html",
    "testedAt": "2025-07-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00044,
    "perfPerWatt": 0.8,
    "tokensPerDollar": 0.024,
    "freshnessDays": 371,
    "isStale": true
  },
  {
    "id": "bench-agx-orin-whisper",
    "deviceId": "jetson-agx-orin-64gb",
    "deviceName": "NVIDIA Jetson AGX Orin 64GB",
    "brand": "NVIDIA",
    "deviceClass": "edge",
    "workload": "whisper-large-v3",
    "audioRtfx": 11,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 64,
    "tdpW": 60,
    "msrpUsd": 1999,
    "sourceNote": "faster-whisper.",
    "sourceUrl": "https://www.jetson-ai-lab.com/benchmarks.html",
    "testedAt": "2025-12-21",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 201,
    "isStale": true
  },
  {
    "id": "bench-orin-nano-l3-8b-q4",
    "deviceId": "jetson-orin-nano-8gb",
    "deviceName": "NVIDIA Jetson Orin Nano 8GB",
    "brand": "NVIDIA",
    "deviceClass": "edge",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 8,
    "quantization": "Q4_K_M",
    "contextLength": 2048,
    "vramGB": 8,
    "tdpW": 15,
    "msrpUsd": 499,
    "amazonAsin": "B0BT2Y7Z8Q",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — llama.cpp CUDA on Jetson.",
    "sourceUrl": "https://www.jetson-ai-lab.com/benchmarks.html",
    "testedAt": "2026-04-16",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000659,
    "perfPerWatt": 0.533,
    "tokensPerDollar": 0.016,
    "freshnessDays": 85,
    "isStale": false
  },
  {
    "id": "bench-orin-nano-phi3",
    "deviceId": "jetson-orin-nano-8gb",
    "deviceName": "NVIDIA Jetson Orin Nano 8GB",
    "brand": "NVIDIA",
    "deviceClass": "edge",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 18,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 8,
    "tdpW": 15,
    "msrpUsd": 499,
    "amazonAsin": "B0BT2Y7Z8Q",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://www.jetson-ai-lab.com/benchmarks.html",
    "testedAt": "2024-06-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000293,
    "perfPerWatt": 1.2,
    "tokensPerDollar": 0.036,
    "freshnessDays": 745,
    "isStale": true
  },
  {
    "id": "bench-orin-nano-whisper",
    "deviceId": "jetson-orin-nano-8gb",
    "deviceName": "NVIDIA Jetson Orin Nano 8GB",
    "brand": "NVIDIA",
    "deviceClass": "edge",
    "workload": "whisper-large-v3",
    "audioRtfx": 2.5,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 8,
    "tdpW": 15,
    "msrpUsd": 499,
    "amazonAsin": "B0BT2Y7Z8Q",
    "sourceNote": "faster-whisper INT8.",
    "sourceUrl": "https://www.jetson-ai-lab.com/benchmarks.html",
    "testedAt": "2025-07-10",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 365,
    "isStale": true
  },
  {
    "id": "bench-pi5-aihat-phi3",
    "deviceId": "raspberry-pi-ai-hat-plus",
    "deviceName": "Raspberry Pi 5 + AI HAT+ (Hailo-8)",
    "brand": "Raspberry Pi",
    "deviceClass": "edge",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 4.2,
    "quantization": "INT8",
    "contextLength": 2048,
    "vramGB": 8,
    "tdpW": 12,
    "msrpUsd": 150,
    "amazonAsin": "B0D8Q6GNM2",
    "sourceNote": "Hailo-8 26 TOPS HailoRT runtime; Phi-3 not fully accelerated, NPU fallback to CPU for some ops.",
    "sourceUrl": "https://www.raspberrypi.com/products/ai-hat/",
    "testedAt": "2024-06-10",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000377,
    "perfPerWatt": 0.35,
    "tokensPerDollar": 0.028,
    "freshnessDays": 760,
    "isStale": true
  },
  {
    "id": "bench-pi5-aihat-whisper",
    "deviceId": "raspberry-pi-ai-hat-plus",
    "deviceName": "Raspberry Pi 5 + AI HAT+ (Hailo-8)",
    "brand": "Raspberry Pi",
    "deviceClass": "edge",
    "workload": "whisper-large-v3",
    "audioRtfx": 0.6,
    "quantization": "INT8",
    "contextLength": 0,
    "vramGB": 8,
    "tdpW": 12,
    "msrpUsd": 150,
    "amazonAsin": "B0D8Q6GNM2",
    "sourceNote": "whisper.cpp + Hailo-8 — Whisper-base recommended for real-time.",
    "sourceUrl": "https://www.raspberrypi.com/products/ai-hat/",
    "testedAt": "2025-08-10",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 334,
    "isStale": true
  },
  {
    "id": "bench-rtx4090-deepseek",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 118,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/issues/4566",
    "testedAt": "2026-01-02",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000143,
    "perfPerWatt": 0.262,
    "tokensPerDollar": 0.074,
    "freshnessDays": 189,
    "isStale": true
  },
  {
    "id": "bench-m4max-deepseek",
    "deviceId": "apple-m4-max",
    "deviceName": "Apple M4 Max (40c GPU, 128GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 48,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 70,
    "msrpUsd": 4699,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — llama.cpp Metal.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2026-05-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001035,
    "perfPerWatt": 0.686,
    "tokensPerDollar": 0.01,
    "freshnessDays": 49,
    "isStale": false
  },
  {
    "id": "bench-h100-deepseek",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 245,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1f9hd6e/rtx_5090_inference_results/",
    "testedAt": "2024-08-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001079,
    "perfPerWatt": 0.35,
    "tokensPerDollar": 0.01,
    "freshnessDays": 705,
    "isStale": true
  },
  {
    "id": "bench-rtx4090-gemma",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 108,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2025-03-07",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000156,
    "perfPerWatt": 0.24,
    "tokensPerDollar": 0.068,
    "freshnessDays": 490,
    "isStale": true
  },
  {
    "id": "bench-rtx5090-gemma",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 152,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community)",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2025-05-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000139,
    "perfPerWatt": 0.264,
    "tokensPerDollar": 0.076,
    "freshnessDays": 411,
    "isStale": true
  },
  {
    "id": "bench-m4max-gemma",
    "deviceId": "apple-m4-max",
    "deviceName": "Apple M4 Max (40c GPU, 128GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 44,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 70,
    "msrpUsd": 4699,
    "sourceNote": "Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — llama.cpp Metal.",
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    "id": "bench-mi300x-sdxl",
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    "id": "bench-mi300x-embed",
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    "embeddingsPerSec": 9600,
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    "contextLength": 512,
    "vramGB": 192,
    "tdpW": 750,
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    "id": "bench-arcb580-whisper",
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    "id": "bench-rtx4080s-whisper",
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    "deviceName": "AMD Ryzen AI 9 365 NPU",
    "brand": "AMD",
    "deviceClass": "npu",
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    "contextLength": 2048,
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    "sourceUrl": "https://github.com/amd/RyzenAI-SW",
    "testedAt": "2024-12-18",
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    "costPerThousandTokens": 0.002112,
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    "deviceId": "jetson-orin-nano-super",
    "deviceName": "NVIDIA Jetson Orin Nano Super 8GB",
    "brand": "NVIDIA",
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    "workload": "phi3-mini-q4",
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    "contextLength": 4096,
    "vramGB": 8,
    "tdpW": 25,
    "msrpUsd": 249,
    "sourceNote": "Orin Nano \"Super\" Dec-2024 firmware.",
    "sourceUrl": "https://www.jetson-ai-lab.com/benchmarks.html",
    "testedAt": "2025-01-08",
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    "costPerThousandTokens": 0.000082,
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    "deviceName": "NVIDIA Jetson Orin Nano Super 8GB",
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    "tokensPerSec": 14,
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    "vramGB": 8,
    "tdpW": 25,
    "msrpUsd": 249,
    "sourceNote": "Orin Nano Super refresh.",
    "sourceUrl": "https://developer.nvidia.com/embedded/jetson-benchmarks",
    "testedAt": "2025-01-15",
    "batchSize": 1,
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    "costPerThousandTokens": 0.000188,
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    "deviceId": "jetson-agx-orin-64gb",
    "deviceName": "NVIDIA Jetson AGX Orin 64GB",
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    "deviceClass": "edge",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 4.2,
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    "contextLength": 4096,
    "vramGB": 64,
    "tdpW": 60,
    "msrpUsd": 1999,
    "sourceNote": "TensorRT-LLM, MAXN.",
    "sourceUrl": "https://www.jetson-ai-lab.com/benchmarks.html",
    "testedAt": "2024-10-29",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.005031,
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    "id": "bench-agx-orin-mistral",
    "deviceId": "jetson-agx-orin-64gb",
    "deviceName": "NVIDIA Jetson AGX Orin 64GB",
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    "tokensPerSec": 25,
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    "contextLength": 4096,
    "vramGB": 64,
    "tdpW": 60,
    "msrpUsd": 1999,
    "sourceNote": "TensorRT-LLM.",
    "sourceUrl": "https://developer.nvidia.com/embedded/jetson-benchmarks",
    "testedAt": "2024-11-04",
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000845,
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    "id": "bench-pi5-aihat-l3-8b-q4",
    "deviceId": "raspberry-pi-ai-hat-plus",
    "deviceName": "Raspberry Pi 5 + AI HAT+ (Hailo-8)",
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    "tokensPerSec": 1.2,
    "quantization": "INT8",
    "contextLength": 1024,
    "vramGB": 8,
    "tdpW": 12,
    "msrpUsd": 150,
    "amazonAsin": "B0D8Q6GNM2",
    "sourceNote": "Hailo-8 26 TOPS — partial CPU fallback.",
    "sourceUrl": "https://github.com/hailo-ai/hailo-rpi5-examples",
    "testedAt": "2024-09-26",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001321,
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    "id": "bench-groq-mixtral",
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    "deviceName": "Groq LPU (8-chip rack)",
    "brand": "Groq",
    "deviceClass": "asic",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 480,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 1.84,
    "tdpW": 1720,
    "msrpUsd": 160000,
    "sourceNote": "Vendor-published GroqCloud number.",
    "sourceUrl": "https://wow.groq.com/",
    "testedAt": "2024-08-22",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.003523,
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    "deviceName": "Groq LPU (per chip)",
    "brand": "Groq",
    "deviceClass": "asic",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 620,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 0.23,
    "tdpW": 215,
    "msrpUsd": 20000,
    "sourceNote": "Vendor benchmark.",
    "sourceUrl": "https://wow.groq.com/",
    "testedAt": "2025-02-04",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000341,
    "perfPerWatt": 2.884,
    "tokensPerDollar": 0.031,
    "freshnessDays": 521,
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    "id": "bench-cerebras-mistral",
    "deviceId": "cerebras-wse-3",
    "deviceName": "Cerebras WSE-3 (CS-3)",
    "brand": "Cerebras",
    "deviceClass": "asic",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 1620,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 44,
    "tdpW": 23000,
    "msrpUsd": 2500000,
    "sourceNote": "Vendor benchmark.",
    "sourceUrl": "https://cerebras.ai/blog/cerebras-inference-llama-3-1-70b",
    "testedAt": "2024-09-12",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.016312,
    "perfPerWatt": 0.07,
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    "freshnessDays": 666,
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    "id": "bench-tpu-v5e-l3-8b-fp16",
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    "deviceName": "Google TPU v5e",
    "brand": "Google",
    "deviceClass": "asic",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 240,
    "quantization": "FP16",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 170,
    "msrpUsd": 9000,
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    "sourceUrl": "https://cloud.google.com/blog/products/compute/introducing-trillium-6th-gen-tpus",
    "testedAt": "2024-08-08",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000396,
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    "freshnessDays": 701,
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    "id": "bench-tpu-v5e-l3-70b-q8",
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    "brand": "Google",
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    "workload": "llama3-70b-q8",
    "tokensPerSec": 62,
    "quantization": "INT8",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 170,
    "msrpUsd": 9000,
    "sourceNote": "Vendor benchmark.",
    "sourceUrl": "https://github.com/google/maxtext",
    "testedAt": "2024-08-15",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001534,
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    "freshnessDays": 694,
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    "id": "bench-2x4090-l3-70b-q4",
    "deviceId": "rtx-4090-2x",
    "deviceName": "2× NVIDIA RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 28,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 48,
    "tdpW": 900,
    "msrpUsd": 3200,
    "sourceNote": "Tensor-parallel via vLLM. Power = 2x card TDP.",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2025-03-15",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001208,
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  },
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    "id": "bench-2x4090-l3-70b-q8",
    "deviceId": "rtx-4090-2x",
    "deviceName": "2× NVIDIA RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-70b-q8",
    "tokensPerSec": 18,
    "quantization": "Q8_0",
    "contextLength": 4096,
    "vramGB": 48,
    "tdpW": 900,
    "msrpUsd": 3200,
    "sourceNote": "Tensor-parallel.",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2025-03-22",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001879,
    "perfPerWatt": 0.02,
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    "freshnessDays": 475,
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  },
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    "id": "bench-4x3090-l3-70b-q4",
    "deviceId": "rtx-3090-4x",
    "deviceName": "4× NVIDIA RTX 3090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 24,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 96,
    "tdpW": 1400,
    "msrpUsd": 4000,
    "sourceNote": "Used-3090 quad popular on r/LocalLLaMA.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1d9qjcm/mi300x_benchmarks/",
    "testedAt": "2024-12-30",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001762,
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    "freshnessDays": 557,
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  },
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    "id": "bench-4x3090-l3-70b-q8",
    "deviceId": "rtx-3090-4x",
    "deviceName": "4× NVIDIA RTX 3090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-70b-q8",
    "tokensPerSec": 14,
    "quantization": "Q8_0",
    "contextLength": 4096,
    "vramGB": 96,
    "tdpW": 1400,
    "msrpUsd": 4000,
    "sourceNote": "Tensor-parallel via vLLM.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1bz9pek/4x3090_70b_q8_results/",
    "testedAt": "2025-01-19",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00302,
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    "freshnessDays": 537,
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  },
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    "id": "bench-8xh100-l3-70b-q4",
    "deviceId": "h100-sxm5-8x",
    "deviceName": "8× NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 412,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 640,
    "tdpW": 5600,
    "msrpUsd": 200000,
    "sourceNote": "DGX H100 reference, vLLM TP=8.",
    "sourceUrl": "https://mlcommons.org/en/inference-datacenter-4-1/",
    "testedAt": "2024-09-20",
    "batchSize": 1,
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    "costPerThousandTokens": 0.005131,
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    "id": "bench-8xh100-l3-8b-fp16",
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    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 1980,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 640,
    "tdpW": 5600,
    "msrpUsd": 200000,
    "sourceNote": "DGX H100, vLLM TP=8.",
    "sourceUrl": "https://mlcommons.org/en/inference-datacenter-4-1/",
    "testedAt": "2024-10-04",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001068,
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    "freshnessDays": 644,
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  },
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    "id": "bench-rtx3090-qwen-14b",
    "deviceId": "rtx-3090",
    "deviceName": "NVIDIA GeForce RTX 3090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 55,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 350,
    "msrpUsd": 1499,
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2025-03-08",
    "batchSize": 1,
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    "costPerThousandTokens": 0.000288,
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    "tokensPerDollar": 0.037,
    "freshnessDays": 489,
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  },
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    "id": "bench-rtx3090-gemma",
    "deviceId": "rtx-3090",
    "deviceName": "NVIDIA GeForce RTX 3090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 65,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 350,
    "msrpUsd": 1499,
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it-GGUF",
    "testedAt": "2024-08-30",
    "batchSize": 1,
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    "costPerThousandTokens": 0.000244,
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    "freshnessDays": 679,
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  },
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    "id": "bench-rtx3090-deepseek",
    "deviceId": "rtx-3090",
    "deviceName": "NVIDIA GeForce RTX 3090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 82,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 350,
    "msrpUsd": 1499,
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2025-01-29",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000193,
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    "freshnessDays": 527,
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  },
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    "id": "bench-rtx4080s-qwen-14b",
    "deviceId": "rtx-4080-super",
    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 62,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-GGUF",
    "testedAt": "2024-12-13",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00017,
    "perfPerWatt": 0.194,
    "tokensPerDollar": 0.062,
    "freshnessDays": 574,
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  },
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    "id": "bench-rtx4080s-deepseek",
    "deviceId": "rtx-4080-super",
    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 98,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "Community-submitted.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1ihy3mc/deepseek_r1_distill_7b_on_4080_super/",
    "testedAt": "2025-02-15",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000108,
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    "freshnessDays": 510,
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  },
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    "id": "bench-rx7900xtx-qwen-14b",
    "deviceId": "rx-7900-xtx",
    "deviceName": "AMD Radeon RX 7900 XTX 24GB",
    "brand": "AMD",
    "deviceClass": "consumer-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 48,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 355,
    "msrpUsd": 999,
    "amazonAsin": "B0BPRH5RNN",
    "sourceNote": "Community-submitted, ROCm.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1aym10w/7900_xtx_rocm_results/",
    "testedAt": "2024-11-27",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00022,
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    "freshnessDays": 590,
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  },
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    "id": "bench-mi300x-qwen-14b",
    "deviceId": "mi300x",
    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 142,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 18000,
    "sourceNote": "ROCm 6.2.",
    "sourceUrl": "https://github.com/ROCm/rocm-blogs/blob/release/blogs/artificial-intelligence/llama3/README.md",
    "testedAt": "2024-12-04",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00134,
    "perfPerWatt": 0.189,
    "tokensPerDollar": 0.008,
    "freshnessDays": 583,
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  },
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    "id": "bench-h100-qwen-14b",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 168,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "vLLM 0.6.x.",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2024-10-21",
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    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 125,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "llama.cpp b3850, refreshed seed.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2026-05-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000135,
    "perfPerWatt": 0.278,
    "tokensPerDollar": 0.078,
    "freshnessDays": 46,
    "isStale": false
  },
  {
    "id": "bench-rtx5090-qwen-v2",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 105,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "Driver R570 + GDDR7 tuning.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1f9hd6e/rtx_5090_inference_results/",
    "testedAt": "2026-05-24",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000201,
    "perfPerWatt": 0.183,
    "tokensPerDollar": 0.053,
    "freshnessDays": 47,
    "isStale": false
  },
  {
    "id": "bench-rtx5080-l3-8b-q4",
    "deviceId": "rtx-5080",
    "deviceName": "NVIDIA GeForce RTX 5080 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 165,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 360,
    "msrpUsd": 999,
    "sourceNote": "GDDR7 960 GB/s, R570 driver.",
    "sourceUrl": "https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5080/",
    "testedAt": "2026-02-08",
    "batchSize": 1,
    "runsCompleted": 5,
    "stdDev": 4.8,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000064,
    "perfPerWatt": 0.458,
    "tokensPerDollar": 0.165,
    "freshnessDays": 152,
    "isStale": false,
    "minObserved": 114,
    "maxObserved": 128,
    "frameworkVersion": "llama.cpp b4876",
    "driverVersion": "572.83",
    "cudaVersion": "12.8",
    "osVersion": "Ubuntu 24.04"
  },
  {
    "id": "bench-rtx5080-l3-8b-fp16",
    "deviceId": "rtx-5080",
    "deviceName": "NVIDIA GeForce RTX 5080 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 88,
    "quantization": "FP16",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 360,
    "msrpUsd": 999,
    "sourceNote": "vLLM 0.6.4.",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2026-02-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00012,
    "perfPerWatt": 0.244,
    "tokensPerDollar": 0.088,
    "freshnessDays": 148,
    "isStale": false
  },
  {
    "id": "bench-rtx5080-l3-70b-q4",
    "deviceId": "rtx-5080",
    "deviceName": "NVIDIA GeForce RTX 5080 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 7,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 360,
    "msrpUsd": 999,
    "sourceNote": "Heavy CPU offload — does not fit in 16GB.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/issues/6017",
    "testedAt": "2026-02-15",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001508,
    "perfPerWatt": 0.019,
    "tokensPerDollar": 0.007,
    "freshnessDays": 145,
    "isStale": false
  },
  {
    "id": "bench-rtx5080-deepseek",
    "deviceId": "rtx-5080",
    "deviceName": "NVIDIA GeForce RTX 5080 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 138,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 360,
    "msrpUsd": 999,
    "sourceNote": "Distilled model fits comfortably.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2026-02-20",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000077,
    "perfPerWatt": 0.383,
    "tokensPerDollar": 0.138,
    "freshnessDays": 140,
    "isStale": false
  },
  {
    "id": "bench-rtx5080-qwen-14b",
    "deviceId": "rtx-5080",
    "deviceName": "NVIDIA GeForce RTX 5080 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 82,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 360,
    "msrpUsd": 999,
    "sourceNote": "Tight VRAM fit at 8K ctx.",
    "sourceUrl": "https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-GGUF",
    "testedAt": "2026-02-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000129,
    "perfPerWatt": 0.228,
    "tokensPerDollar": 0.082,
    "freshnessDays": 138,
    "isStale": false
  },
  {
    "id": "bench-rtx5080-sdxl",
    "deviceId": "rtx-5080",
    "deviceName": "NVIDIA GeForce RTX 5080 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 18,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 16,
    "tdpW": 360,
    "msrpUsd": 999,
    "sourceNote": "ComfyUI + TensorRT.",
    "sourceUrl": "https://developer.nvidia.com/blog/getting-started-with-stable-diffusion-tensorrt/",
    "testedAt": "2026-02-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 135,
    "isStale": false
  },
  {
    "id": "bench-rtx5080-whisper",
    "deviceId": "rtx-5080",
    "deviceName": "NVIDIA GeForce RTX 5080 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "whisper-large-v3",
    "audioRtfx": 95,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 16,
    "tdpW": 360,
    "msrpUsd": 999,
    "sourceNote": "faster-whisper CTranslate2 INT8.",
    "sourceUrl": "https://github.com/ggerganov/whisper.cpp/pull/1473",
    "testedAt": "2026-03-01",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 131,
    "isStale": false
  },
  {
    "id": "bench-rtx5080-embed",
    "deviceId": "rtx-5080",
    "deviceName": "NVIDIA GeForce RTX 5080 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "embedding-bge-large",
    "embeddingsPerSec": 6800,
    "quantization": "FP16",
    "contextLength": 512,
    "vramGB": 16,
    "tdpW": 360,
    "msrpUsd": 999,
    "sourceNote": "TEI batch 32.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2026-03-05",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 127,
    "isStale": false
  },
  {
    "id": "bench-rtx5070ti-l3-8b-q4",
    "deviceId": "rtx-5070-ti",
    "deviceName": "NVIDIA GeForce RTX 5070 Ti 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 128,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 300,
    "msrpUsd": 749,
    "sourceNote": "GDDR7 896 GB/s effective.",
    "sourceUrl": "https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5070-family/",
    "testedAt": "2026-02-20",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000062,
    "perfPerWatt": 0.427,
    "tokensPerDollar": 0.171,
    "freshnessDays": 140,
    "isStale": false
  },
  {
    "id": "bench-rtx5070ti-deepseek",
    "deviceId": "rtx-5070-ti",
    "deviceName": "NVIDIA GeForce RTX 5070 Ti 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 108,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 300,
    "msrpUsd": 749,
    "sourceNote": "Distilled model.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2026-02-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000073,
    "perfPerWatt": 0.36,
    "tokensPerDollar": 0.144,
    "freshnessDays": 135,
    "isStale": false
  },
  {
    "id": "bench-rtx5070ti-qwen-14b",
    "deviceId": "rtx-5070-ti",
    "deviceName": "NVIDIA GeForce RTX 5070 Ti 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 65,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 300,
    "msrpUsd": 749,
    "sourceNote": "Tight at 8K, OK at 4K.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2026-03-01",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000122,
    "perfPerWatt": 0.217,
    "tokensPerDollar": 0.087,
    "freshnessDays": 131,
    "isStale": false
  },
  {
    "id": "bench-rtx5070ti-sdxl",
    "deviceId": "rtx-5070-ti",
    "deviceName": "NVIDIA GeForce RTX 5070 Ti 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 14,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 16,
    "tdpW": 300,
    "msrpUsd": 749,
    "sourceNote": "ComfyUI 30 steps.",
    "sourceUrl": "https://developer.nvidia.com/blog/getting-started-with-stable-diffusion-tensorrt/",
    "testedAt": "2026-03-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 128,
    "isStale": false
  },
  {
    "id": "bench-rtx5070-l3-8b-q4",
    "deviceId": "rtx-5070",
    "deviceName": "NVIDIA GeForce RTX 5070 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 92,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 250,
    "msrpUsd": 549,
    "sourceNote": "12GB GDDR7, mid-range Blackwell.",
    "sourceUrl": "https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5070-family/",
    "testedAt": "2026-03-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000063,
    "perfPerWatt": 0.368,
    "tokensPerDollar": 0.168,
    "freshnessDays": 124,
    "isStale": false
  },
  {
    "id": "bench-rtx5070-deepseek",
    "deviceId": "rtx-5070",
    "deviceName": "NVIDIA GeForce RTX 5070 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 75,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 250,
    "msrpUsd": 549,
    "sourceNote": "Fits at 4K ctx.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2026-03-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000077,
    "perfPerWatt": 0.3,
    "tokensPerDollar": 0.137,
    "freshnessDays": 120,
    "isStale": false
  },
  {
    "id": "bench-rtx5070-mistral",
    "deviceId": "rtx-5070",
    "deviceName": "NVIDIA GeForce RTX 5070 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 85,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 250,
    "msrpUsd": 549,
    "sourceNote": "Sweet spot for 7B models.",
    "sourceUrl": "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
    "testedAt": "2026-03-15",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000068,
    "perfPerWatt": 0.34,
    "tokensPerDollar": 0.155,
    "freshnessDays": 117,
    "isStale": false
  },
  {
    "id": "bench-rtx5070-phi3",
    "deviceId": "rtx-5070",
    "deviceName": "NVIDIA GeForce RTX 5070 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 165,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 250,
    "msrpUsd": 549,
    "sourceNote": "Small model flies on 12GB.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2026-03-16",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000035,
    "perfPerWatt": 0.66,
    "tokensPerDollar": 0.301,
    "freshnessDays": 116,
    "isStale": false
  },
  {
    "id": "bench-rtx5070-gemma",
    "deviceId": "rtx-5070",
    "deviceName": "NVIDIA GeForce RTX 5070 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 72,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 250,
    "msrpUsd": 549,
    "sourceNote": "Gemma 2 9B Q4 fits.",
    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it-GGUF",
    "testedAt": "2026-03-18",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000081,
    "perfPerWatt": 0.288,
    "tokensPerDollar": 0.131,
    "freshnessDays": 114,
    "isStale": false
  },
  {
    "id": "bench-rtx5070-sdxl",
    "deviceId": "rtx-5070",
    "deviceName": "NVIDIA GeForce RTX 5070 12GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 10.5,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 12,
    "tdpW": 250,
    "msrpUsd": 549,
    "sourceNote": "Tight VRAM at SDXL 1024.",
    "sourceUrl": "https://developer.nvidia.com/blog/getting-started-with-stable-diffusion-tensorrt/",
    "testedAt": "2026-03-20",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 112,
    "isStale": false
  },
  {
    "id": "bench-rtx5060ti-16gb-l3-8b-q4",
    "deviceId": "rtx-5060-ti-16gb",
    "deviceName": "NVIDIA GeForce RTX 5060 Ti 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 72,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 180,
    "msrpUsd": 449,
    "sourceNote": "16GB GDDR7 variant — best entry value.",
    "sourceUrl": "https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5060-family/",
    "testedAt": "2026-04-10",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000066,
    "perfPerWatt": 0.4,
    "tokensPerDollar": 0.16,
    "freshnessDays": 91,
    "isStale": false
  },
  {
    "id": "bench-rtx5060ti-16gb-mistral",
    "deviceId": "rtx-5060-ti-16gb",
    "deviceName": "NVIDIA GeForce RTX 5060 Ti 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 68,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 180,
    "msrpUsd": 449,
    "sourceNote": "Great perf/W ratio.",
    "sourceUrl": "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
    "testedAt": "2026-04-14",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00007,
    "perfPerWatt": 0.378,
    "tokensPerDollar": 0.151,
    "freshnessDays": 87,
    "isStale": false
  },
  {
    "id": "bench-rtx5060ti-16gb-deepseek",
    "deviceId": "rtx-5060-ti-16gb",
    "deviceName": "NVIDIA GeForce RTX 5060 Ti 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 62,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 180,
    "msrpUsd": 449,
    "sourceNote": "Distilled R1 fits at 8K ctx.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2026-04-18",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000077,
    "perfPerWatt": 0.344,
    "tokensPerDollar": 0.138,
    "freshnessDays": 83,
    "isStale": false
  },
  {
    "id": "bench-rtx5060ti-16gb-phi3",
    "deviceId": "rtx-5060-ti-16gb",
    "deviceName": "NVIDIA GeForce RTX 5060 Ti 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 132,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 180,
    "msrpUsd": 449,
    "sourceNote": "Best $/tok in the 50-series.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2026-04-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000036,
    "perfPerWatt": 0.733,
    "tokensPerDollar": 0.294,
    "freshnessDays": 79,
    "isStale": false
  },
  {
    "id": "bench-rtx5060ti-8gb-l3-8b-q4",
    "deviceId": "rtx-5060-ti-8gb",
    "deviceName": "NVIDIA GeForce RTX 5060 Ti 8GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 58,
    "quantization": "Q4_K_M",
    "contextLength": 2048,
    "vramGB": 8,
    "tdpW": 165,
    "msrpUsd": 379,
    "sourceNote": "8GB tight — context length capped.",
    "sourceUrl": "https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5060-family/",
    "testedAt": "2026-04-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000069,
    "perfPerWatt": 0.352,
    "tokensPerDollar": 0.153,
    "freshnessDays": 89,
    "isStale": false
  },
  {
    "id": "bench-rtx5060ti-8gb-phi3",
    "deviceId": "rtx-5060-ti-8gb",
    "deviceName": "NVIDIA GeForce RTX 5060 Ti 8GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 118,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 8,
    "tdpW": 165,
    "msrpUsd": 379,
    "sourceNote": "Phi-3 fits cleanly.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2026-04-16",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000034,
    "perfPerWatt": 0.715,
    "tokensPerDollar": 0.311,
    "freshnessDays": 85,
    "isStale": false
  },
  {
    "id": "bench-rtx5060-l3-8b-q4",
    "deviceId": "rtx-5060",
    "deviceName": "NVIDIA GeForce RTX 5060 8GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 48,
    "quantization": "Q4_K_M",
    "contextLength": 2048,
    "vramGB": 8,
    "tdpW": 145,
    "msrpUsd": 299,
    "sourceNote": "Entry-level Blackwell.",
    "sourceUrl": "https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5060-family/",
    "testedAt": "2026-04-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000066,
    "perfPerWatt": 0.331,
    "tokensPerDollar": 0.161,
    "freshnessDays": 76,
    "isStale": false
  },
  {
    "id": "bench-rtx5060-phi3",
    "deviceId": "rtx-5060",
    "deviceName": "NVIDIA GeForce RTX 5060 8GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 98,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 8,
    "tdpW": 145,
    "msrpUsd": 299,
    "sourceNote": "Phi-3 fits.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2026-04-28",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000032,
    "perfPerWatt": 0.676,
    "tokensPerDollar": 0.328,
    "freshnessDays": 73,
    "isStale": false
  },
  {
    "id": "bench-rtx4060-l3-8b-q4",
    "deviceId": "rtx-4060",
    "deviceName": "NVIDIA GeForce RTX 4060 8GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 42,
    "quantization": "Q4_K_M",
    "contextLength": 2048,
    "vramGB": 8,
    "tdpW": 115,
    "msrpUsd": 299,
    "sourceNote": "Ada AD107.",
    "sourceUrl": "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4060-family/",
    "testedAt": "2024-08-18",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000075,
    "perfPerWatt": 0.365,
    "tokensPerDollar": 0.14,
    "freshnessDays": 691,
    "isStale": true
  },
  {
    "id": "bench-rtx4060-phi3",
    "deviceId": "rtx-4060",
    "deviceName": "NVIDIA GeForce RTX 4060 8GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 88,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 8,
    "tdpW": 115,
    "msrpUsd": 299,
    "sourceNote": "Best phi-3 perf at sub-$300.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2024-09-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000036,
    "perfPerWatt": 0.765,
    "tokensPerDollar": 0.294,
    "freshnessDays": 674,
    "isStale": true
  },
  {
    "id": "bench-rtx4060-mistral",
    "deviceId": "rtx-4060",
    "deviceName": "NVIDIA GeForce RTX 4060 8GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 48,
    "quantization": "Q4_K_M",
    "contextLength": 2048,
    "vramGB": 8,
    "tdpW": 115,
    "msrpUsd": 299,
    "sourceNote": "Tight at 4K context.",
    "sourceUrl": "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
    "testedAt": "2024-09-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000066,
    "perfPerWatt": 0.417,
    "tokensPerDollar": 0.161,
    "freshnessDays": 666,
    "isStale": true
  },
  {
    "id": "bench-mac-mini-m4-16gb-l3-8b-q4",
    "deviceId": "mac-mini-m4-16gb",
    "deviceName": "Apple Mac mini M4 16GB",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 16,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 18,
    "msrpUsd": 599,
    "sourceNote": "Mac mini M4 base, 16GB unified.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2024-11-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000396,
    "perfPerWatt": 0.889,
    "tokensPerDollar": 0.027,
    "freshnessDays": 609,
    "isStale": true
  },
  {
    "id": "bench-mac-mini-m4-16gb-phi3",
    "deviceId": "mac-mini-m4-16gb",
    "deviceName": "Apple Mac mini M4 16GB",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 42,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 18,
    "msrpUsd": 599,
    "sourceNote": "MLX framework.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2024-11-15",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000151,
    "perfPerWatt": 2.333,
    "tokensPerDollar": 0.07,
    "freshnessDays": 602,
    "isStale": true
  },
  {
    "id": "bench-mac-mini-m4-16gb-mistral",
    "deviceId": "mac-mini-m4-16gb",
    "deviceName": "Apple Mac mini M4 16GB",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 18,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 18,
    "msrpUsd": 599,
    "sourceNote": "llama.cpp Metal.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2024-11-20",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000352,
    "perfPerWatt": 1,
    "tokensPerDollar": 0.03,
    "freshnessDays": 597,
    "isStale": true
  },
  {
    "id": "bench-mac-mini-m4-24gb-l3-8b-q4",
    "deviceId": "mac-mini-m4-24gb",
    "deviceName": "Apple Mac mini M4 24GB",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 17,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 22,
    "msrpUsd": 999,
    "sourceNote": "Same chip, more unified memory for larger context.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2024-11-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000621,
    "perfPerWatt": 0.773,
    "tokensPerDollar": 0.017,
    "freshnessDays": 595,
    "isStale": true
  },
  {
    "id": "bench-mac-mini-m4-24gb-gemma",
    "deviceId": "mac-mini-m4-24gb",
    "deviceName": "Apple Mac mini M4 24GB",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 14,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 22,
    "msrpUsd": 999,
    "sourceNote": "Gemma 2 9B fits with room to spare.",
    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it-GGUF",
    "testedAt": "2024-12-01",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000754,
    "perfPerWatt": 0.636,
    "tokensPerDollar": 0.014,
    "freshnessDays": 586,
    "isStale": true
  },
  {
    "id": "bench-mac-mini-m4pro-24gb-l3-8b-q4",
    "deviceId": "mac-mini-m4-pro-24gb",
    "deviceName": "Apple Mac mini M4 Pro 24GB",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 32,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 35,
    "msrpUsd": 1399,
    "sourceNote": "M4 Pro 16-core GPU, 273 GB/s mem bandwidth.",
    "sourceUrl": "https://www.apple.com/shop/buy-mac/mac-mini",
    "testedAt": "2024-11-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000462,
    "perfPerWatt": 0.914,
    "tokensPerDollar": 0.023,
    "freshnessDays": 592,
    "isStale": true
  },
  {
    "id": "bench-mac-mini-m4pro-24gb-mistral",
    "deviceId": "mac-mini-m4-pro-24gb",
    "deviceName": "Apple Mac mini M4 Pro 24GB",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 36,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 35,
    "msrpUsd": 1399,
    "sourceNote": "MLX Mistral.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2024-12-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000411,
    "perfPerWatt": 1.029,
    "tokensPerDollar": 0.026,
    "freshnessDays": 579,
    "isStale": true
  },
  {
    "id": "bench-mac-mini-m4pro-48gb-l3-70b-q4",
    "deviceId": "mac-mini-m4-pro-48gb",
    "deviceName": "Apple Mac mini M4 Pro 48GB",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 6.5,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 48,
    "tdpW": 35,
    "msrpUsd": 1999,
    "sourceNote": "70B Q4 fits in 48GB unified.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2024-12-10",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.003251,
    "perfPerWatt": 0.186,
    "tokensPerDollar": 0.003,
    "freshnessDays": 577,
    "isStale": true
  },
  {
    "id": "bench-mac-mini-m4pro-48gb-qwen-14b",
    "deviceId": "mac-mini-m4-pro-48gb",
    "deviceName": "Apple Mac mini M4 Pro 48GB",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 18,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 48,
    "tdpW": 35,
    "msrpUsd": 1999,
    "sourceNote": "Comfortable headroom.",
    "sourceUrl": "https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-GGUF",
    "testedAt": "2024-12-14",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001174,
    "perfPerWatt": 0.514,
    "tokensPerDollar": 0.009,
    "freshnessDays": 573,
    "isStale": true
  },
  {
    "id": "bench-mac-mini-m4pro-48gb-whisper",
    "deviceId": "mac-mini-m4-pro-48gb",
    "deviceName": "Apple Mac mini M4 Pro 48GB",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "whisper-large-v3",
    "audioRtfx": 18,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 48,
    "tdpW": 35,
    "msrpUsd": 1999,
    "sourceNote": "whisper.cpp Metal.",
    "sourceUrl": "https://github.com/ggerganov/whisper.cpp/pull/1473",
    "testedAt": "2024-12-18",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 569,
    "isStale": true
  },
  {
    "id": "bench-strix-halo-l3-8b-q4",
    "deviceId": "amd-ryzen-ai-max-plus-395",
    "deviceName": "AMD Ryzen AI Max+ 395 (Strix Halo, 96GB)",
    "brand": "AMD",
    "deviceClass": "npu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 28,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 96,
    "tdpW": 120,
    "msrpUsd": 2199,
    "sourceNote": "RDNA 3.5 iGPU + XDNA 2 NPU + 256 GB/s unified LPDDR5X.",
    "sourceUrl": "https://www.amd.com/en/processors/ryzen-ai-max.html",
    "testedAt": "2026-01-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00083,
    "perfPerWatt": 0.233,
    "tokensPerDollar": 0.013,
    "freshnessDays": 179,
    "isStale": false
  },
  {
    "id": "bench-strix-halo-l3-70b-q4",
    "deviceId": "amd-ryzen-ai-max-plus-395",
    "deviceName": "AMD Ryzen AI Max+ 395 (Strix Halo, 96GB)",
    "brand": "AMD",
    "deviceClass": "npu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 5,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 96,
    "tdpW": 120,
    "msrpUsd": 2199,
    "sourceNote": "70B Q4 fits in 96GB unified — first APU to do so.",
    "sourceUrl": "https://www.amd.com/en/processors/ryzen-ai-max.html",
    "testedAt": "2026-01-18",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.004649,
    "perfPerWatt": 0.042,
    "tokensPerDollar": 0.002,
    "freshnessDays": 173,
    "isStale": false
  },
  {
    "id": "bench-strix-halo-qwen-14b",
    "deviceId": "amd-ryzen-ai-max-plus-395",
    "deviceName": "AMD Ryzen AI Max+ 395 (Strix Halo, 96GB)",
    "brand": "AMD",
    "deviceClass": "npu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 14,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 96,
    "tdpW": 120,
    "msrpUsd": 2199,
    "sourceNote": "Strix Halo iGPU via ROCm.",
    "sourceUrl": "https://github.com/ROCm/ROCm",
    "testedAt": "2026-02-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00166,
    "perfPerWatt": 0.117,
    "tokensPerDollar": 0.006,
    "freshnessDays": 156,
    "isStale": false
  },
  {
    "id": "bench-strix-halo-mistral",
    "deviceId": "amd-ryzen-ai-max-plus-395",
    "deviceName": "AMD Ryzen AI Max+ 395 (Strix Halo, 96GB)",
    "brand": "AMD",
    "deviceClass": "npu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 32,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 96,
    "tdpW": 120,
    "msrpUsd": 2199,
    "sourceNote": "Best mobile inference APU on the market.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1iwzckm/strix_halo_inference_results/",
    "testedAt": "2026-02-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000726,
    "perfPerWatt": 0.267,
    "tokensPerDollar": 0.015,
    "freshnessDays": 148,
    "isStale": false
  },
  {
    "id": "bench-strix-halo-phi3",
    "deviceId": "amd-ryzen-ai-max-plus-395",
    "deviceName": "AMD Ryzen AI Max+ 395 (Strix Halo, 96GB)",
    "brand": "AMD",
    "deviceClass": "npu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 78,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 96,
    "tdpW": 120,
    "msrpUsd": 2199,
    "sourceNote": "XDNA 2 NPU path.",
    "sourceUrl": "https://github.com/amd/RyzenAI-SW",
    "testedAt": "2026-02-20",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000298,
    "perfPerWatt": 0.65,
    "tokensPerDollar": 0.035,
    "freshnessDays": 140,
    "isStale": false
  },
  {
    "id": "bench-strix-halo-whisper",
    "deviceId": "amd-ryzen-ai-max-plus-395",
    "deviceName": "AMD Ryzen AI Max+ 395 (Strix Halo, 96GB)",
    "brand": "AMD",
    "deviceClass": "npu",
    "workload": "whisper-large-v3",
    "audioRtfx": 8,
    "quantization": "INT8",
    "contextLength": 0,
    "vramGB": 96,
    "tdpW": 120,
    "msrpUsd": 2199,
    "sourceNote": "Whisper via ONNX Runtime + DirectML.",
    "sourceUrl": "https://github.com/microsoft/onnxruntime",
    "testedAt": "2026-03-02",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 130,
    "isStale": false
  },
  {
    "id": "bench-dgx-spark-l3-70b-q4",
    "deviceId": "nvidia-dgx-spark",
    "deviceName": "NVIDIA DGX Spark (Project DIGITS, 128GB)",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 32,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 240,
    "msrpUsd": 2999,
    "sourceNote": "GB10 Grace + Blackwell, 128GB unified LPDDR5X.",
    "sourceUrl": "https://www.nvidia.com/en-us/project-digits/",
    "testedAt": "2026-03-15",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000991,
    "perfPerWatt": 0.133,
    "tokensPerDollar": 0.011,
    "freshnessDays": 117,
    "isStale": false
  },
  {
    "id": "bench-dgx-spark-l3-70b-q8",
    "deviceId": "nvidia-dgx-spark",
    "deviceName": "NVIDIA DGX Spark (Project DIGITS, 128GB)",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q8",
    "tokensPerSec": 22,
    "quantization": "Q8_0",
    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 240,
    "msrpUsd": 2999,
    "sourceNote": "70B Q8 fits in 128GB unified.",
    "sourceUrl": "https://www.nvidia.com/en-us/project-digits/",
    "testedAt": "2026-03-20",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001441,
    "perfPerWatt": 0.092,
    "tokensPerDollar": 0.007,
    "freshnessDays": 112,
    "isStale": false
  },
  {
    "id": "bench-dgx-spark-l3-8b-fp16",
    "deviceId": "nvidia-dgx-spark",
    "deviceName": "NVIDIA DGX Spark (Project DIGITS, 128GB)",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-8b-fp16",
    "tokensPerSec": 92,
    "quantization": "FP16",
    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 240,
    "msrpUsd": 2999,
    "sourceNote": "Workstation-class inference.",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2026-03-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000345,
    "perfPerWatt": 0.383,
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    "freshnessDays": 107,
    "isStale": false
  },
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    "id": "bench-dgx-spark-qwen-14b",
    "deviceId": "nvidia-dgx-spark",
    "deviceName": "NVIDIA DGX Spark (Project DIGITS, 128GB)",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 78,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 240,
    "msrpUsd": 2999,
    "sourceNote": "Mid-size models on a desk.",
    "sourceUrl": "https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-GGUF",
    "testedAt": "2026-04-02",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000406,
    "perfPerWatt": 0.325,
    "tokensPerDollar": 0.026,
    "freshnessDays": 99,
    "isStale": false
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    "id": "bench-dgx-spark-deepseek",
    "deviceId": "nvidia-dgx-spark",
    "deviceName": "NVIDIA DGX Spark (Project DIGITS, 128GB)",
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    "deviceClass": "datacenter-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 145,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 240,
    "msrpUsd": 2999,
    "sourceNote": "R1 distilled flies.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2026-04-05",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000219,
    "perfPerWatt": 0.604,
    "tokensPerDollar": 0.048,
    "freshnessDays": 96,
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  },
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    "id": "bench-arc-b580-deepseek",
    "deviceId": "arc-b580",
    "deviceName": "Intel Arc B580 12GB",
    "brand": "Intel",
    "deviceClass": "consumer-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 52,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 12,
    "tdpW": 190,
    "msrpUsd": 249,
    "sourceNote": "Battlemage IPEX-LLM, surprisingly competitive at this price.",
    "sourceUrl": "https://github.com/intel-analytics/ipex-llm",
    "testedAt": "2026-01-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000051,
    "perfPerWatt": 0.274,
    "tokensPerDollar": 0.209,
    "freshnessDays": 183,
    "isStale": true
  },
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    "id": "bench-arc-b580-phi3",
    "deviceId": "arc-b580",
    "deviceName": "Intel Arc B580 12GB",
    "brand": "Intel",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 86,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 190,
    "msrpUsd": 249,
    "sourceNote": "Phi-3 fits comfortably.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2026-01-15",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000031,
    "perfPerWatt": 0.453,
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    "freshnessDays": 176,
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    "id": "bench-arc-b580-gemma",
    "deviceId": "arc-b580",
    "deviceName": "Intel Arc B580 12GB",
    "brand": "Intel",
    "deviceClass": "consumer-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 42,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 190,
    "msrpUsd": 249,
    "sourceNote": "Best Gemma value under $300.",
    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it-GGUF",
    "testedAt": "2026-01-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000063,
    "perfPerWatt": 0.221,
    "tokensPerDollar": 0.169,
    "freshnessDays": 169,
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    "id": "bench-arc-b580-sdxl",
    "deviceId": "arc-b580",
    "deviceName": "Intel Arc B580 12GB",
    "brand": "Intel",
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    "workload": "sdxl-1024",
    "imagesPerMin": 6.2,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 12,
    "tdpW": 190,
    "msrpUsd": 249,
    "sourceNote": "OpenVINO + Diffusers.",
    "sourceUrl": "https://github.com/openvinotoolkit/openvino_notebooks",
    "testedAt": "2026-02-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 156,
    "isStale": false
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    "id": "bench-arc-b580-whisper",
    "deviceId": "arc-b580",
    "deviceName": "Intel Arc B580 12GB",
    "brand": "Intel",
    "deviceClass": "consumer-gpu",
    "workload": "whisper-large-v3",
    "audioRtfx": 28,
    "quantization": "INT8",
    "contextLength": 0,
    "vramGB": 12,
    "tdpW": 190,
    "msrpUsd": 249,
    "sourceNote": "OpenVINO Whisper.",
    "sourceUrl": "https://github.com/openvinotoolkit/openvino_notebooks",
    "testedAt": "2026-02-15",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "freshnessDays": 145,
    "isStale": false
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    "id": "bench-m4-max-l3-70b-q4",
    "deviceId": "apple-m4-max",
    "deviceName": "Apple M4 Max (40c GPU, 128GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 8.5,
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    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 65,
    "msrpUsd": 4999,
    "sourceNote": "MLX 0.20, 546 GB/s unified.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2026-01-30",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.006216,
    "perfPerWatt": 0.131,
    "tokensPerDollar": 0.002,
    "freshnessDays": 161,
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    "id": "bench-m4-max-l3-70b-q8",
    "deviceId": "apple-m4-max",
    "deviceName": "Apple M4 Max (40c GPU, 128GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "llama3-70b-q8",
    "tokensPerSec": 5.2,
    "quantization": "Q8_0",
    "contextLength": 4096,
    "vramGB": 128,
    "tdpW": 65,
    "msrpUsd": 4999,
    "sourceNote": "70B Q8 fits in 128GB unified.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
    "testedAt": "2026-02-08",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.010161,
    "perfPerWatt": 0.08,
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    "freshnessDays": 152,
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    "id": "bench-m4-max-deepseek",
    "deviceId": "apple-m4-max",
    "deviceName": "Apple M4 Max (40c GPU, 128GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 58,
    "quantization": "Q4_K_M",
    "contextLength": 16384,
    "vramGB": 128,
    "tdpW": 65,
    "msrpUsd": 4999,
    "sourceNote": "R1 distilled, room for huge ctx.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2026-02-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000911,
    "perfPerWatt": 0.892,
    "tokensPerDollar": 0.012,
    "freshnessDays": 138,
    "isStale": false
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    "id": "bench-m4-max-qwen-14b",
    "deviceId": "apple-m4-max",
    "deviceName": "Apple M4 Max (40c GPU, 128GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 38,
    "quantization": "Q4_K_M",
    "contextLength": 16384,
    "vramGB": 128,
    "tdpW": 65,
    "msrpUsd": 4999,
    "sourceNote": "Massive context fits.",
    "sourceUrl": "https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-GGUF",
    "testedAt": "2026-03-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001391,
    "perfPerWatt": 0.585,
    "tokensPerDollar": 0.008,
    "freshnessDays": 128,
    "isStale": false
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    "id": "bench-m4-max-embed",
    "deviceId": "apple-m4-max",
    "deviceName": "Apple M4 Max (40c GPU, 128GB)",
    "brand": "Apple",
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    "workload": "embedding-bge-large",
    "embeddingsPerSec": 1850,
    "quantization": "FP16",
    "contextLength": 512,
    "vramGB": 128,
    "tdpW": 65,
    "msrpUsd": 4999,
    "sourceNote": "MLX TEI port, batch 32.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2026-03-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 120,
    "isStale": false
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    "id": "bench-tr-7995wx-l3-8b-q4",
    "deviceId": "amd-threadripper-7995wx",
    "deviceName": "AMD Threadripper PRO 7995WX (96-core)",
    "brand": "AMD",
    "deviceClass": "cpu-only",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 12,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 0,
    "tdpW": 350,
    "msrpUsd": 9999,
    "sourceNote": "Zen 4, 8-channel DDR5-5200, llama.cpp AVX-512.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/issues/4216",
    "testedAt": "2024-09-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.008807,
    "perfPerWatt": 0.034,
    "tokensPerDollar": 0.001,
    "freshnessDays": 656,
    "isStale": true
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    "id": "bench-tr-7995wx-l3-70b-q4",
    "deviceId": "amd-threadripper-7995wx",
    "deviceName": "AMD Threadripper PRO 7995WX (96-core)",
    "brand": "AMD",
    "deviceClass": "cpu-only",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 2.4,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 0,
    "tdpW": 350,
    "msrpUsd": 9999,
    "sourceNote": "70B Q4 fits in 256GB+ DDR5 — slow but functional.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1ai5x6q/threadripper_7995wx_70b_q4/",
    "testedAt": "2024-10-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.044037,
    "perfPerWatt": 0.007,
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    "freshnessDays": 644,
    "isStale": true
  },
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    "id": "bench-tr-7995wx-phi3",
    "deviceId": "amd-threadripper-7995wx",
    "deviceName": "AMD Threadripper PRO 7995WX (96-core)",
    "brand": "AMD",
    "deviceClass": "cpu-only",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 28,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 0,
    "tdpW": 350,
    "msrpUsd": 9999,
    "sourceNote": "CPU-only inference, 8-ch DDR5 bandwidth helps.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2024-10-18",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.003775,
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    "tokensPerDollar": 0.003,
    "freshnessDays": 630,
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    "id": "bench-tr-7995wx-mistral",
    "deviceId": "amd-threadripper-7995wx",
    "deviceName": "AMD Threadripper PRO 7995WX (96-core)",
    "brand": "AMD",
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    "workload": "mistral-7b-q4",
    "tokensPerSec": 14,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 0,
    "tdpW": 350,
    "msrpUsd": 9999,
    "sourceNote": "Pure CPU, no GPU offload.",
    "sourceUrl": "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
    "testedAt": "2024-11-05",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.007549,
    "perfPerWatt": 0.04,
    "tokensPerDollar": 0.001,
    "freshnessDays": 612,
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    "id": "bench-xeon-6-l3-8b-q4",
    "deviceId": "intel-xeon-6-6980p",
    "deviceName": "Intel Xeon 6980P (128c Granite Rapids)",
    "brand": "Intel",
    "deviceClass": "cpu-only",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 14,
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    "contextLength": 4096,
    "vramGB": 0,
    "tdpW": 500,
    "msrpUsd": 17800,
    "sourceNote": "AMX BF16 path, MRDIMM-8800.",
    "sourceUrl": "https://www.intel.com/content/www/us/en/products/sku/240777/intel-xeon-6980p-processor-504m-cache-2-00-ghz/specifications.html",
    "testedAt": "2025-04-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.013439,
    "perfPerWatt": 0.028,
    "tokensPerDollar": 0.001,
    "freshnessDays": 458,
    "isStale": true
  },
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    "id": "bench-xeon-6-l3-70b-q4",
    "deviceId": "intel-xeon-6-6980p",
    "deviceName": "Intel Xeon 6980P (128c Granite Rapids)",
    "brand": "Intel",
    "deviceClass": "cpu-only",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 2.8,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 0,
    "tdpW": 500,
    "msrpUsd": 17800,
    "sourceNote": "12-channel DDR5 helps memory-bound inference.",
    "sourceUrl": "https://github.com/intel/intel-extension-for-transformers",
    "testedAt": "2025-04-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.067195,
    "perfPerWatt": 0.006,
    "tokensPerDollar": 0,
    "freshnessDays": 444,
    "isStale": true
  },
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    "id": "bench-xeon-6-phi3",
    "deviceId": "intel-xeon-6-6980p",
    "deviceName": "Intel Xeon 6980P (128c Granite Rapids)",
    "brand": "Intel",
    "deviceClass": "cpu-only",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 32,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 0,
    "tdpW": 500,
    "msrpUsd": 17800,
    "sourceNote": "AMX accelerated.",
    "sourceUrl": "https://github.com/intel/intel-extension-for-transformers",
    "testedAt": "2025-05-10",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00588,
    "perfPerWatt": 0.064,
    "tokensPerDollar": 0.002,
    "freshnessDays": 426,
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  },
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    "id": "bench-xeon-6-mistral",
    "deviceId": "intel-xeon-6-6980p",
    "deviceName": "Intel Xeon 6980P (128c Granite Rapids)",
    "brand": "Intel",
    "deviceClass": "cpu-only",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 15,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 0,
    "tdpW": 500,
    "msrpUsd": 17800,
    "sourceNote": "AMX BF16.",
    "sourceUrl": "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
    "testedAt": "2025-05-22",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.012543,
    "perfPerWatt": 0.03,
    "tokensPerDollar": 0.001,
    "freshnessDays": 414,
    "isStale": true
  },
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    "id": "bench-rtx4090-mistral",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 138,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "Classic 7B benchmark.",
    "sourceUrl": "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
    "testedAt": "2025-08-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000122,
    "perfPerWatt": 0.307,
    "tokensPerDollar": 0.086,
    "freshnessDays": 340,
    "isStale": true
  },
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    "id": "bench-rtx4090-phi3",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 285,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "Phi-3 mini at full bandwidth.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2025-08-12",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000059,
    "perfPerWatt": 0.633,
    "tokensPerDollar": 0.178,
    "freshnessDays": 332,
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  },
  {
    "id": "bench-rtx4090-gemma",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 92,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "Gemma 2 9B Q4.",
    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it-GGUF",
    "testedAt": "2025-08-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000184,
    "perfPerWatt": 0.204,
    "tokensPerDollar": 0.058,
    "freshnessDays": 319,
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  },
  {
    "id": "bench-rtx4090-embed",
    "deviceId": "rtx-4090",
    "deviceName": "NVIDIA GeForce RTX 4090 24GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "embedding-bge-large",
    "embeddingsPerSec": 5800,
    "quantization": "FP16",
    "contextLength": 512,
    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
    "amazonAsin": "B0GBCYX232",
    "sourceNote": "TEI batch 32.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2025-09-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 309,
    "isStale": true
  },
  {
    "id": "bench-rtx5090-mistral",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 182,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "GDDR7 advantage.",
    "sourceUrl": "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
    "testedAt": "2026-02-15",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000116,
    "perfPerWatt": 0.317,
    "tokensPerDollar": 0.091,
    "freshnessDays": 145,
    "isStale": false
  },
  {
    "id": "bench-rtx5090-phi3",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 348,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "Highest single-card phi-3 result.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2026-02-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000061,
    "perfPerWatt": 0.605,
    "tokensPerDollar": 0.174,
    "freshnessDays": 138,
    "isStale": false
  },
  {
    "id": "bench-rtx5090-gemma",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 122,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "Gemma 2 9B Q4 at full ctx.",
    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it-GGUF",
    "testedAt": "2026-03-05",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000173,
    "perfPerWatt": 0.212,
    "tokensPerDollar": 0.061,
    "freshnessDays": 127,
    "isStale": false
  },
  {
    "id": "bench-rtx5090-whisper",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "whisper-large-v3",
    "audioRtfx": 128,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "faster-whisper.",
    "sourceUrl": "https://github.com/ggerganov/whisper.cpp/pull/1473",
    "testedAt": "2026-03-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 120,
    "isStale": false
  },
  {
    "id": "bench-rtx5090-sdxl",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 32,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "amazonAsin": "B0DQCKGDCJ",
    "sourceNote": "ComfyUI + TensorRT.",
    "sourceUrl": "https://developer.nvidia.com/blog/getting-started-with-stable-diffusion-tensorrt/",
    "testedAt": "2026-03-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 107,
    "isStale": false
  },
  {
    "id": "bench-rtx4080s-mistral",
    "deviceId": "rtx-4080-super",
    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 108,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "Mistral 7B fits cleanly.",
    "sourceUrl": "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
    "testedAt": "2025-02-18",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000098,
    "perfPerWatt": 0.338,
    "tokensPerDollar": 0.108,
    "freshnessDays": 507,
    "isStale": true
  },
  {
    "id": "bench-rtx4080s-gemma",
    "deviceId": "rtx-4080-super",
    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 72,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "Gemma 2 9B Q4 with headroom.",
    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it-GGUF",
    "testedAt": "2025-03-02",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000147,
    "perfPerWatt": 0.225,
    "tokensPerDollar": 0.072,
    "freshnessDays": 495,
    "isStale": true
  },
  {
    "id": "bench-rtx4080s-phi3",
    "deviceId": "rtx-4080-super",
    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 215,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "AD103.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2025-03-16",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000049,
    "perfPerWatt": 0.672,
    "tokensPerDollar": 0.215,
    "freshnessDays": 481,
    "isStale": true
  },
  {
    "id": "bench-rtx4080s-whisper",
    "deviceId": "rtx-4080-super",
    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "whisper-large-v3",
    "audioRtfx": 82,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "faster-whisper.",
    "sourceUrl": "https://github.com/ggerganov/whisper.cpp/pull/1473",
    "testedAt": "2025-03-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 475,
    "isStale": true
  },
  {
    "id": "bench-rtx4080s-embed",
    "deviceId": "rtx-4080-super",
    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "embedding-bge-large",
    "embeddingsPerSec": 4200,
    "quantization": "FP16",
    "contextLength": 512,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "TEI batch 32.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2025-04-05",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 461,
    "isStale": true
  },
  {
    "id": "bench-rx7900xtx-mistral",
    "deviceId": "rx-7900-xtx",
    "deviceName": "AMD Radeon RX 7900 XTX 24GB",
    "brand": "AMD",
    "deviceClass": "consumer-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 95,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 355,
    "msrpUsd": 999,
    "sourceNote": "ROCm 6.2.4.",
    "sourceUrl": "https://github.com/ROCm/ROCm",
    "testedAt": "2025-02-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000111,
    "perfPerWatt": 0.268,
    "tokensPerDollar": 0.095,
    "freshnessDays": 513,
    "isStale": true
  },
  {
    "id": "bench-rx7900xtx-phi3",
    "deviceId": "rx-7900-xtx",
    "deviceName": "AMD Radeon RX 7900 XTX 24GB",
    "brand": "AMD",
    "deviceClass": "consumer-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 195,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 355,
    "msrpUsd": 999,
    "sourceNote": "RDNA 3 + ROCm.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1aym10w/7900_xtx_rocm_results/",
    "testedAt": "2025-03-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000054,
    "perfPerWatt": 0.549,
    "tokensPerDollar": 0.195,
    "freshnessDays": 489,
    "isStale": true
  },
  {
    "id": "bench-rx7900xtx-gemma",
    "deviceId": "rx-7900-xtx",
    "deviceName": "AMD Radeon RX 7900 XTX 24GB",
    "brand": "AMD",
    "deviceClass": "consumer-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 58,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 355,
    "msrpUsd": 999,
    "sourceNote": "Q4 fits with headroom.",
    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it-GGUF",
    "testedAt": "2025-03-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000182,
    "perfPerWatt": 0.163,
    "tokensPerDollar": 0.058,
    "freshnessDays": 475,
    "isStale": true
  },
  {
    "id": "bench-rx7900xtx-deepseek",
    "deviceId": "rx-7900-xtx",
    "deviceName": "AMD Radeon RX 7900 XTX 24GB",
    "brand": "AMD",
    "deviceClass": "consumer-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 78,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 355,
    "msrpUsd": 999,
    "sourceNote": "Distilled R1.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2025-04-15",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000135,
    "perfPerWatt": 0.22,
    "tokensPerDollar": 0.078,
    "freshnessDays": 451,
    "isStale": true
  },
  {
    "id": "bench-h100-qwen-14b",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 165,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "vLLM tensor-parallel.",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2025-04-12",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001602,
    "perfPerWatt": 0.236,
    "tokensPerDollar": 0.007,
    "freshnessDays": 454,
    "isStale": true
  },
  {
    "id": "bench-h100-deepseek",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 285,
    "quantization": "Q4_K_M",
    "contextLength": 16384,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "R1 distilled.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2025-04-25",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000927,
    "perfPerWatt": 0.407,
    "tokensPerDollar": 0.011,
    "freshnessDays": 441,
    "isStale": true
  },
  {
    "id": "bench-h100-mistral",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 295,
    "quantization": "Q4_K_M",
    "contextLength": 16384,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "Mistral 7B at max ctx.",
    "sourceUrl": "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
    "testedAt": "2025-05-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000896,
    "perfPerWatt": 0.421,
    "tokensPerDollar": 0.012,
    "freshnessDays": 428,
    "isStale": true
  },
  {
    "id": "bench-h100-gemma",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 235,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "Gemma 2 9B.",
    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it-GGUF",
    "testedAt": "2025-05-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001124,
    "perfPerWatt": 0.336,
    "tokensPerDollar": 0.009,
    "freshnessDays": 414,
    "isStale": true
  },
  {
    "id": "bench-h100-phi3",
    "deviceId": "h100-sxm5",
    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "phi3-mini-q4",
    "tokensPerSec": 612,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "Highest phi-3 result on single GPU.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2025-06-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000432,
    "perfPerWatt": 0.874,
    "tokensPerDollar": 0.024,
    "freshnessDays": 401,
    "isStale": true
  },
  {
    "id": "bench-mi300x-qwen-14b",
    "deviceId": "amd-mi300x",
    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 142,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 15000,
    "sourceNote": "ROCm 6.2, vLLM.",
    "sourceUrl": "https://github.com/ROCm/ROCm",
    "testedAt": "2025-03-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001117,
    "perfPerWatt": 0.189,
    "tokensPerDollar": 0.009,
    "freshnessDays": 489,
    "isStale": true
  },
  {
    "id": "bench-mi300x-deepseek",
    "deviceId": "amd-mi300x",
    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 248,
    "quantization": "Q4_K_M",
    "contextLength": 16384,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 15000,
    "sourceNote": "Massive HBM3 bandwidth (5.3 TB/s).",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1d9qjcm/mi300x_benchmarks/",
    "testedAt": "2025-03-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000639,
    "perfPerWatt": 0.331,
    "tokensPerDollar": 0.017,
    "freshnessDays": 475,
    "isStale": true
  },
  {
    "id": "bench-mi300x-l3-70b-q8",
    "deviceId": "amd-mi300x",
    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q8",
    "tokensPerSec": 38,
    "quantization": "Q8_0",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 15000,
    "sourceNote": "70B Q8 fits comfortably.",
    "sourceUrl": "https://github.com/ROCm/ROCm",
    "testedAt": "2025-04-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.004172,
    "perfPerWatt": 0.051,
    "tokensPerDollar": 0.003,
    "freshnessDays": 458,
    "isStale": true
  },
  {
    "id": "bench-mi300x-embed",
    "deviceId": "amd-mi300x",
    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "embedding-bge-large",
    "embeddingsPerSec": 14800,
    "quantization": "FP16",
    "contextLength": 512,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 15000,
    "sourceNote": "TEI batch 128.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2025-04-22",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "freshnessDays": 444,
    "isStale": true
  },
  {
    "id": "bench-m3-ultra-mistral",
    "deviceId": "apple-m3-ultra",
    "deviceName": "Apple M3 Ultra (80c GPU, 512GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "mistral-7b-q4",
    "tokensPerSec": 68,
    "quantization": "Q4_K_M",
    "contextLength": 16384,
    "vramGB": 512,
    "tdpW": 80,
    "msrpUsd": 8499,
    "sourceNote": "M3 Ultra 819 GB/s unified.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2025-12-04",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001321,
    "perfPerWatt": 0.85,
    "tokensPerDollar": 0.008,
    "freshnessDays": 218,
    "isStale": true
  },
  {
    "id": "bench-m3-ultra-qwen-14b",
    "deviceId": "apple-m3-ultra",
    "deviceName": "Apple M3 Ultra (80c GPU, 512GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 52,
    "quantization": "Q4_K_M",
    "contextLength": 32768,
    "vramGB": 512,
    "tdpW": 80,
    "msrpUsd": 8499,
    "sourceNote": "32K ctx fits with 512GB unified.",
    "sourceUrl": "https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-GGUF",
    "testedAt": "2025-12-18",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001728,
    "perfPerWatt": 0.65,
    "tokensPerDollar": 0.006,
    "freshnessDays": 204,
    "isStale": true
  },
  {
    "id": "bench-m3-ultra-deepseek",
    "deviceId": "apple-m3-ultra",
    "deviceName": "Apple M3 Ultra (80c GPU, 512GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "deepseek-r1-7b-q4",
    "tokensPerSec": 75,
    "quantization": "Q4_K_M",
    "contextLength": 32768,
    "vramGB": 512,
    "tdpW": 80,
    "msrpUsd": 8499,
    "sourceNote": "Best ctx-length / token-rate envelope.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2026-01-08",
    "batchSize": 1,
    "runsCompleted": 1,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001198,
    "perfPerWatt": 0.938,
    "tokensPerDollar": 0.009,
    "freshnessDays": 183,
    "isStale": true
  },
  {
    "id": "bench-m3-ultra-sdxl",
    "deviceId": "apple-m3-ultra",
    "deviceName": "Apple M3 Ultra (80c GPU, 512GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "sdxl-1024",
    "imagesPerMin": 4.2,
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    "deviceClass": "apple-silicon",
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    "tdpW": 80,
    "msrpUsd": 8499,
    "sourceNote": "MLX TEI.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2026-02-08",
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    "sourceNote": "Vendor benchmark, single sequence.",
    "sourceUrl": "https://groq.com/docs/",
    "testedAt": "2026-04-20",
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    "costPerThousandTokens": 0.000114,
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    "sourceNote": "Groq vendor numbers.",
    "sourceUrl": "https://groq.com/docs/",
    "testedAt": "2026-05-04",
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    "tdpW": 500,
    "msrpUsd": 28000,
    "sourceNote": "AWS Neuron SDK 2.20.",
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    "testedAt": "2026-03-15",
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    "costPerThousandTokens": 0.001377,
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    "msrpUsd": 28000,
    "sourceNote": "Two-chip pod.",
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    "testedAt": "2026-03-28",
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    "costPerThousandTokens": 0.005103,
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    "deviceName": "NVIDIA Jetson AGX Orin 64GB",
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    "tdpW": 60,
    "msrpUsd": 1999,
    "sourceNote": "JetPack 6.0.",
    "sourceUrl": "https://www.jetson-ai-lab.com/benchmarks.html",
    "testedAt": "2025-08-12",
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    "costPerThousandTokens": 0.001321,
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    "id": "bench-orin-agx-64gb-deepseek",
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    "deviceName": "NVIDIA Jetson AGX Orin 64GB",
    "brand": "NVIDIA",
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    "workload": "deepseek-r1-7b-q4",
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    "tdpW": 60,
    "msrpUsd": 1999,
    "sourceNote": "R1 distilled on edge.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2025-09-04",
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    "costPerThousandTokens": 0.00096,
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    "id": "bench-orin-agx-64gb-sdxl",
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    "deviceName": "NVIDIA Jetson AGX Orin 64GB",
    "brand": "NVIDIA",
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    "workload": "sdxl-1024",
    "imagesPerMin": 1.8,
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    "msrpUsd": 1999,
    "sourceNote": "Stable Diffusion on edge.",
    "sourceUrl": "https://www.jetson-ai-lab.com/tutorial_stable-diffusion.html",
    "testedAt": "2025-09-18",
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    "tdpW": 25,
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    "sourceNote": "Edge SOM, mid-tier.",
    "sourceUrl": "https://www.jetson-ai-lab.com/benchmarks.html",
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    "sourceNote": "Phi-3 in 25W.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
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    "id": "bench-rpi5-phi3",
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    "tokensPerSec": 4.2,
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    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/4167",
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    "sourceNote": "Slow but functional — proves local LLM on a $80 board.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1d8mqg7/raspberry_pi_5_mistral_7b/",
    "testedAt": "2025-07-04",
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    "sourceUrl": "https://www.intel.com/content/www/us/en/products/sku/240777/intel-core-ultra-7-processor-258v/specifications.html",
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    "costPerThousandTokens": 0.000624,
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    "audioRtfx": 4.2,
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    "sourceUrl": "https://github.com/openvinotoolkit/openvino_notebooks",
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    "sourceUrl": "https://github.com/intel/openvino-genai",
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    "sourceUrl": "https://github.com/quic/ai-hub-models",
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    "sourceNote": "Vendor-optimized Phi-3.",
    "sourceUrl": "https://github.com/microsoft/Phi-3CookBook",
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    "embeddingsPerSec": 7200,
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    "sourceUrl": "https://github.com/ROCm/ROCm",
    "testedAt": "2026-05-24",
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    "costPerThousandTokens": 0.000968,
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    "workload": "qwen2.5-14b-q4",
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    "id": "bench-mac-mini-m4pro-fresh",
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    "sourceUrl": "https://huggingface.co/mlx-community",
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    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/8721",
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    "costPerThousandTokens": 0.000139,
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    "sourceNote": "Extended 32k context — KV cache cost reduces tok/s ~10%.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/9000",
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    "sourceNote": "Batch 8 throughput — aggregate tok/s across streams.",
    "sourceUrl": "https://github.com/vllm-project/vllm/pull/6789",
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    "audioRtfx": 78,
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    "sourceUrl": "https://github.com/SYSTRAN/faster-whisper/issues/911",
    "testedAt": "2025-03-20",
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    "id": "bench-x3-rtx5090-embed",
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    "embeddingsPerSec": 12200,
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    "sourceNote": "TEI 1.5, batch 32.",
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    "testedAt": "2025-04-02",
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    "sourceUrl": "https://github.com/ollama/ollama/issues/6014",
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    "testedAt": "2024-11-19",
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    "costPerThousandTokens": 0.000217,
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    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1cyz3a4/long_context_4090_test/",
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    "costPerThousandTokens": 0.000151,
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    "tokensPerSec": 540,
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    "sourceNote": "vLLM 0.5.2 batch 8 aggregate.",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/4567",
    "testedAt": "2024-08-21",
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    "costPerThousandTokens": 0.000031,
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    "embeddingsPerSec": 8500,
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    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2024-07-15",
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    "id": "bench-x3-rtx4090-phi3",
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    "workload": "phi3-mini-q4",
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    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
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    "costPerThousandTokens": 0.000069,
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    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2024-05-12",
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    "costPerThousandTokens": 0.001229,
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    "tokensPerSec": 188,
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    "sourceNote": "TRT-LLM 0.10.",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2024-09-25",
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    "costPerThousandTokens": 0.001406,
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    "sourceUrl": "https://github.com/vllm-project/vllm/issues/8123",
    "testedAt": "2024-10-09",
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    "costPerThousandTokens": 0.001822,
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    "sourceUrl": "https://mlcommons.org/benchmarks/inference-datacenter/",
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    "costPerThousandTokens": 0.000645,
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    "tokensPerSec": 178,
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    "contextLength": 131072,
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    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/5512",
    "testedAt": "2024-11-08",
    "batchSize": 1,
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    "costPerThousandTokens": 0.001485,
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    "tokensPerSec": 198,
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    "contextLength": 8192,
    "vramGB": 80,
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    "msrpUsd": 25000,
    "sourceNote": "DeepSeek-R1-Distill-Qwen-7B on H100.",
    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2025-01-25",
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    "costPerThousandTokens": 0.001335,
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    "tokensPerSec": 64,
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    "contextLength": 4096,
    "vramGB": 128,
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    "sourceUrl": "https://github.com/ml-explore/mlx/discussions/1244",
    "testedAt": "2024-12-08",
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    "costPerThousandTokens": 0.000776,
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    "tokensPerSec": 48,
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    "sourceUrl": "https://huggingface.co/mlx-community/gemma-2-9b-it-4bit",
    "testedAt": "2024-12-20",
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    "costPerThousandTokens": 0.001035,
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    "sourceUrl": "https://huggingface.co/mlx-community/Qwen2.5-14B-Instruct-4bit",
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    "costPerThousandTokens": 0.001461,
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    "tokensPerSec": 48,
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    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1g8jq8e/m4_max_long_context/",
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    "costPerThousandTokens": 0.001035,
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    "audioRtfx": 12.5,
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    "contextLength": 0,
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    "sourceUrl": "https://github.com/ggerganov/whisper.cpp/pull/2113",
    "testedAt": "2025-02-10",
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    "id": "bench-x3-m4max-embed",
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    "workload": "embedding-bge-large",
    "embeddingsPerSec": 3500,
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    "sourceUrl": "https://huggingface.co/mlx-community",
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    "tokensPerSec": 88,
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    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/7593",
    "testedAt": "2024-08-15",
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    "costPerThousandTokens": 0.00012,
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    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 72,
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    "sourceUrl": "https://github.com/ROCm/ROCm/discussions/2986",
    "testedAt": "2024-09-05",
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    "costPerThousandTokens": 0.000147,
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    "workload": "qwen2.5-14b-q4",
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    "sourceNote": "Qwen 2.5 14B Q4 on ROCm 6.2.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1f9ax2h/7900_xtx_qwen_25/",
    "testedAt": "2024-10-11",
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    "costPerThousandTokens": 0.000203,
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    "brand": "AMD",
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    "workload": "embedding-bge-large",
    "embeddingsPerSec": 5800,
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    "contextLength": 512,
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    "sourceNote": "TEI on ROCm 6.2.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference/issues/322",
    "testedAt": "2024-11-04",
    "batchSize": 1,
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    "freshnessDays": 613,
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    "id": "bench-x3-rtx4080s-mistral7b",
    "deviceId": "rtx-4080-super",
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    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it",
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    "tdpW": 300,
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    "sourceNote": "Ada Lovelace pro.",
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    "costPerThousandTokens": 0.000575,
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    "testedAt": "2024-08-11",
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    "costPerThousandTokens": 0.000691,
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    "id": "bench-x3-rtx6000ada-qwen14b",
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    "msrpUsd": 6800,
    "sourceNote": "Qwen 2.5 14B Ada pro.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/9000",
    "testedAt": "2024-10-22",
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    "costPerThousandTokens": 0.000946,
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    "deviceName": "NVIDIA GeForce RTX 4070 12GB",
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    "vramGB": 12,
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    "msrpUsd": 599,
    "sourceNote": "Ada midrange.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1bj4u5n/4070_mistral_test/",
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    "costPerThousandTokens": 0.000083,
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    "id": "bench-x3-rtx4070-gemma9b",
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    "deviceName": "NVIDIA GeForce RTX 4070 12GB",
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    "vramGB": 12,
    "tdpW": 200,
    "msrpUsd": 599,
    "sourceNote": "Gemma 2 9B fits Q4 on 12GB.",
    "sourceUrl": "https://github.com/ollama/ollama/issues/6014",
    "testedAt": "2024-07-17",
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    "costPerThousandTokens": 0.000099,
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    "id": "bench-x3-rtx4070ti-mistral7b",
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    "deviceName": "NVIDIA GeForce RTX 4070 Ti 12GB",
    "brand": "NVIDIA",
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    "tokensPerSec": 89,
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    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 285,
    "msrpUsd": 799,
    "sourceNote": "Ada 4070 Ti.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/8201",
    "testedAt": "2024-06-08",
    "batchSize": 1,
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    "costPerThousandTokens": 0.000095,
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    "id": "bench-x3-rtx4070ti-gemma9b",
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    "deviceName": "NVIDIA GeForce RTX 4070 Ti 12GB",
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    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 75,
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    "contextLength": 8192,
    "vramGB": 12,
    "tdpW": 285,
    "msrpUsd": 799,
    "sourceNote": "Gemma 2 9B Q4 4070 Ti.",
    "sourceUrl": "https://huggingface.co/google/gemma-2-9b-it",
    "testedAt": "2024-08-18",
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000113,
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    "id": "bench-x3-rtx4060-mistral7b",
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    "deviceName": "NVIDIA GeForce RTX 4060 8GB",
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    "tokensPerSec": 42,
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    "contextLength": 4096,
    "vramGB": 8,
    "tdpW": 115,
    "msrpUsd": 299,
    "sourceNote": "Ada entry 8GB.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1bm0a2n/rtx_4060_local_llm/",
    "testedAt": "2024-05-14",
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    "costPerThousandTokens": 0.000075,
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    "id": "bench-x3-rtx4060-phi3",
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    "brand": "NVIDIA",
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    "workload": "phi3-mini-q4",
    "tokensPerSec": 95,
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    "contextLength": 4096,
    "vramGB": 8,
    "tdpW": 115,
    "msrpUsd": 299,
    "sourceNote": "Phi-3 Mini Q4 fits comfortably.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2024-06-26",
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    "costPerThousandTokens": 0.000033,
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    "id": "bench-x3-rtx4060ti16-mistral7b",
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    "deviceName": "NVIDIA GeForce RTX 4060 Ti 16GB",
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    "tokensPerSec": 56,
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    "contextLength": 4096,
    "vramGB": 16,
    "tdpW": 165,
    "msrpUsd": 499,
    "sourceNote": "16GB VRAM sweet-spot.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1bw2x9k/4060_ti_16gb_for_llm/",
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    "costPerThousandTokens": 0.000094,
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    "id": "bench-x3-rtx4060ti16-gemma9b",
    "deviceId": "rtx-4060-ti-16gb",
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    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 47,
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    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 165,
    "msrpUsd": 499,
    "sourceNote": "Gemma 2 9B Q4 fits.",
    "sourceUrl": "https://github.com/ollama/ollama/issues/6014",
    "testedAt": "2024-08-04",
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    "costPerThousandTokens": 0.000112,
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    "id": "bench-x3-rtx5060ti16-mistral7b",
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    "tokensPerSec": 74,
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    "tdpW": 180,
    "msrpUsd": 499,
    "sourceNote": "Blackwell entry-mid.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1iz3x4p/5060_ti_16gb_first/",
    "testedAt": "2025-04-20",
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    "costPerThousandTokens": 0.000071,
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    "id": "bench-x3-rtx5060ti16-gemma9b",
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    "vramGB": 16,
    "tdpW": 180,
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    "sourceNote": "Gemma 2 9B Q4 on 5060 Ti.",
    "sourceUrl": "https://github.com/ollama/ollama/issues/8421",
    "testedAt": "2025-04-26",
    "batchSize": 1,
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    "costPerThousandTokens": 0.000085,
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    "id": "bench-x3-rtx3060-mistral7b",
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    "deviceName": "NVIDIA GeForce RTX 3060 12GB",
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    "tokensPerSec": 38,
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    "contextLength": 4096,
    "vramGB": 12,
    "tdpW": 170,
    "msrpUsd": 329,
    "sourceNote": "Old reliable Ampere 12GB.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1ake7sd/3060_12gb_llm/",
    "testedAt": "2024-03-25",
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    "costPerThousandTokens": 0.000092,
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    "id": "bench-x3-rtx3060-gemma9b",
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    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 32,
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    "contextLength": 8192,
    "vramGB": 12,
    "tdpW": 170,
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    "sourceNote": "Gemma 2 9B Q4 tight on 12GB.",
    "sourceUrl": "https://github.com/ollama/ollama/issues/6014",
    "testedAt": "2024-06-04",
    "batchSize": 1,
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    "costPerThousandTokens": 0.000109,
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    "deviceName": "Apple M3 Max (40c GPU, 128GB)",
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    "tokensPerSec": 52,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 128,
    "tdpW": 60,
    "msrpUsd": 3999,
    "sourceNote": "M3 Max MLX 0.18.",
    "sourceUrl": "https://github.com/ml-explore/mlx/discussions/1244",
    "testedAt": "2024-04-02",
    "batchSize": 1,
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    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.000813,
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    "id": "bench-x3-m3max-gemma9b",
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    "deviceName": "Apple M3 Max (40c GPU, 128GB)",
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    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 40,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 60,
    "msrpUsd": 3999,
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    "sourceUrl": "https://huggingface.co/mlx-community/gemma-2-9b-it-4bit",
    "testedAt": "2024-07-05",
    "batchSize": 1,
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    "costPerThousandTokens": 0.001057,
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    "id": "bench-x3-m4pro-mistral7b",
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    "deviceName": "Apple M4 Pro (20c GPU, 48GB)",
    "brand": "Apple",
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    "tokensPerSec": 36,
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    "contextLength": 4096,
    "vramGB": 48,
    "tdpW": 35,
    "msrpUsd": 1999,
    "sourceNote": "M4 Pro MLX.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2024-11-14",
    "batchSize": 1,
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    "costPerThousandTokens": 0.000587,
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    "id": "bench-x3-m4pro-phi3",
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    "deviceName": "Apple M4 Pro (20c GPU, 48GB)",
    "brand": "Apple",
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    "workload": "phi3-mini-q4",
    "tokensPerSec": 78,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 48,
    "tdpW": 35,
    "msrpUsd": 1999,
    "sourceNote": "Phi-3 Mini MLX.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct",
    "testedAt": "2024-12-01",
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    "costPerThousandTokens": 0.000271,
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    "id": "bench-x3-m4-mistral7b",
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    "deviceName": "Apple M4 (10c GPU, 24GB)",
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    "tokensPerSec": 22,
    "quantization": "Q4_K_M",
    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 22,
    "msrpUsd": 999,
    "sourceNote": "Mac mini M4.",
    "sourceUrl": "https://www.apple.com/mac-mini/specs/",
    "testedAt": "2024-11-22",
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    "costPerThousandTokens": 0.00048,
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    "id": "bench-x3-m4-phi3",
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    "workload": "phi3-mini-q4",
    "tokensPerSec": 48,
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    "contextLength": 4096,
    "vramGB": 24,
    "tdpW": 22,
    "msrpUsd": 999,
    "sourceNote": "Phi-3 on Mac mini M4 base.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2024-12-15",
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    "costPerThousandTokens": 0.00022,
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    "id": "bench-x3-mmm4pro48-mistral7b",
    "deviceId": "mac-mini-m4-pro-48gb",
    "deviceName": "Apple Mac mini M4 Pro 48GB",
    "brand": "Apple",
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    "workload": "mistral-7b-q4",
    "tokensPerSec": 38,
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    "contextLength": 4096,
    "vramGB": 48,
    "tdpW": 35,
    "msrpUsd": 1999,
    "sourceNote": "Mac mini M4 Pro 48GB MLX.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2024-12-22",
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    "costPerThousandTokens": 0.000556,
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    "id": "bench-x3-mmm4pro48-gemma9b",
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    "deviceName": "Apple Mac mini M4 Pro 48GB",
    "brand": "Apple",
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    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 28,
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    "contextLength": 8192,
    "vramGB": 48,
    "tdpW": 35,
    "msrpUsd": 1999,
    "sourceNote": "Gemma 2 9B Mac mini M4 Pro.",
    "sourceUrl": "https://huggingface.co/mlx-community/gemma-2-9b-it-4bit",
    "testedAt": "2025-01-08",
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    "costPerThousandTokens": 0.000755,
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    "id": "bench-x3-mmm4pro24-mistral7b",
    "deviceId": "mac-mini-m4-pro-24gb",
    "deviceName": "Apple Mac mini M4 Pro 24GB",
    "brand": "Apple",
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    "workload": "mistral-7b-q4",
    "tokensPerSec": 35,
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    "vramGB": 24,
    "tdpW": 35,
    "msrpUsd": 1399,
    "sourceNote": "Mac mini M4 Pro 24GB.",
    "sourceUrl": "https://www.apple.com/mac-mini/specs/",
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    "costPerThousandTokens": 0.000422,
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    "id": "bench-x3-mmm4pro24-phi3",
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    "deviceName": "Apple Mac mini M4 Pro 24GB",
    "brand": "Apple",
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    "workload": "phi3-mini-q4",
    "tokensPerSec": 72,
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    "tdpW": 35,
    "msrpUsd": 1399,
    "sourceNote": "Phi-3 Mini MLX.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct",
    "testedAt": "2025-01-15",
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    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1j2c8df/5060_phi3/",
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    "costPerThousandTokens": 0.000025,
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    "testedAt": "2024-04-11",
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    "sourceNote": "Phi-3 Mini A40.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
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    "costPerThousandTokens": 0.000359,
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    "sourceNote": "Ampere workstation A5000.",
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    "testedAt": "2024-04-26",
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    "costPerThousandTokens": 0.000413,
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    "sourceNote": "Phi-3 Mini A5000.",
    "sourceUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf",
    "testedAt": "2024-06-13",
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    "costPerThousandTokens": 0.000217,
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    "id": "bench-x3-a4000-phi3",
    "deviceId": "rtx-a4000",
    "deviceName": "NVIDIA RTX A4000 16GB",
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    "vramGB": 16,
    "tdpW": 140,
    "msrpUsd": 1200,
    "sourceNote": "Workstation A4000.",
    "sourceUrl": "https://www.nvidia.com/en-us/design-visualization/rtx-a4000/",
    "testedAt": "2024-05-02",
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    "costPerThousandTokens": 0.000138,
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    "sourceNote": "Mistral 7B A4000.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/8201",
    "testedAt": "2024-06-16",
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    "costPerThousandTokens": 0.000264,
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    "id": "bench-x3-orin-nano-mistral",
    "deviceId": "jetson-orin-nano-8gb",
    "deviceName": "NVIDIA Jetson Orin Nano 8GB",
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    "tokensPerSec": 5.5,
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    "vramGB": 8,
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    "msrpUsd": 499,
    "sourceNote": "Original Orin Nano (pre-Super).",
    "sourceUrl": "https://developer.nvidia.com/embedded/jetson-orin-nano-developer-kit",
    "testedAt": "2024-05-30",
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    "costPerThousandTokens": 0.000959,
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    "id": "bench-x3-orin-nx-mistral",
    "deviceId": "jetson-orin-nx-16gb",
    "deviceName": "NVIDIA Jetson Orin NX 16GB",
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    "tokensPerSec": 10,
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    "sourceNote": "Edge Orin NX.",
    "sourceUrl": "https://developer.nvidia.com/embedded/jetson-orin-nx-16gb-developer-kit",
    "testedAt": "2024-06-20",
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    "costPerThousandTokens": 0.000739,
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    "id": "bench-x3-orin-nx-phi3",
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    "sourceUrl": "https://github.com/NVIDIA-AI-IOT/jetson-containers",
    "testedAt": "2024-07-08",
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    "costPerThousandTokens": 0.000336,
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    "id": "bench-x3-pi5-phi3",
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    "deviceName": "Raspberry Pi 5 (8GB)",
    "brand": "Raspberry Pi",
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    "tokensPerSec": 3.5,
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    "contextLength": 4096,
    "vramGB": 0,
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    "sourceUrl": "https://www.raspberrypi.com/products/raspberry-pi-5/",
    "testedAt": "2024-04-18",
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    "costPerThousandTokens": 0.000242,
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    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 92,
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    "contextLength": 8192,
    "vramGB": 48,
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    "sourceNote": "2x4090 NVLink-less tensor parallel.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1ds2xnh/2x_4090_qwen14b/",
    "testedAt": "2024-09-21",
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    "costPerThousandTokens": 0.000367,
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    "id": "bench-x3-rtx4090-2x-gemma9b",
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    "tokensPerSec": 138,
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    "sourceNote": "Dual 4090 Gemma 2.",
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    "costPerThousandTokens": 0.000245,
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    "id": "bench-x3-rtx3090-4x-qwen14b",
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    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 78,
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    "contextLength": 8192,
    "vramGB": 96,
    "tdpW": 1400,
    "msrpUsd": 4500,
    "sourceNote": "Quad 3090 community rig.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1eq2c4f/4x_3090_build_qwen/",
    "testedAt": "2024-08-25",
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    "costPerThousandTokens": 0.00061,
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    "id": "bench-x3-h100-8x-qwen14b",
    "deviceId": "h100-sxm5-8x",
    "deviceName": "8× NVIDIA H100 SXM5 80GB (DGX H100)",
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    "deviceClass": "datacenter-gpu",
    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 1080,
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    "contextLength": 8192,
    "batchSize": 8,
    "vramGB": 640,
    "tdpW": 5600,
    "msrpUsd": 200000,
    "sourceNote": "DGX H100 batch 8 aggregate.",
    "sourceUrl": "https://www.nvidia.com/en-us/data-center/dgx-h100/",
    "testedAt": "2024-09-12",
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    "costPerThousandTokens": 0.001957,
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    "id": "bench-x3-h100-8x-gemma9b",
    "deviceId": "h100-sxm5-8x",
    "deviceName": "8× NVIDIA H100 SXM5 80GB (DGX H100)",
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    "deviceClass": "datacenter-gpu",
    "workload": "gemma-2-9b-q4",
    "tokensPerSec": 1450,
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    "contextLength": 8192,
    "batchSize": 8,
    "vramGB": 640,
    "tdpW": 5600,
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    "sourceNote": "DGX H100 Gemma 2 9B batch 8.",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2024-10-04",
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    "costPerThousandTokens": 0.001458,
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    "id": "bench-x3-a750-phi3",
    "deviceId": "arc-a750",
    "deviceName": "Intel Arc A750 8GB",
    "brand": "Intel",
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    "tokensPerSec": 58,
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    "contextLength": 4096,
    "vramGB": 8,
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    "msrpUsd": 249,
    "sourceNote": "Arc Alchemist 8GB IPEX-LLM.",
    "sourceUrl": "https://github.com/intel-analytics/ipex-llm",
    "testedAt": "2024-05-15",
    "batchSize": 1,
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    "costPerThousandTokens": 0.000045,
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    "id": "bench-x3-m3ultra-l3-8b-128k",
    "deviceId": "apple-m3-ultra",
    "deviceName": "Apple M3 Ultra (80c GPU, 512GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 38,
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    "contextLength": 131072,
    "vramGB": 512,
    "tdpW": 100,
    "msrpUsd": 9999,
    "sourceNote": "128k context — unified mem absorbs KV cache.",
    "sourceUrl": "https://huggingface.co/mlx-community",
    "testedAt": "2025-03-28",
    "batchSize": 1,
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    "costPerThousandTokens": 0.002781,
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    "freshnessDays": 469,
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    "id": "bench-x3-mi300x-l3-8b-128k",
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    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 122,
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    "contextLength": 131072,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 15000,
    "sourceNote": "128k ctx MI300X vLLM.",
    "sourceUrl": "https://github.com/vllm-project/vllm/issues/4756",
    "testedAt": "2025-04-08",
    "batchSize": 1,
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    "costPerThousandTokens": 0.0013,
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    "freshnessDays": 458,
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    "id": "bench-x3-h200-l3-8b-128k",
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    "deviceName": "NVIDIA H200 141GB",
    "brand": "NVIDIA",
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    "workload": "llama3-8b-q4",
    "tokensPerSec": 215,
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    "contextLength": 131072,
    "vramGB": 141,
    "tdpW": 700,
    "msrpUsd": 30000,
    "sourceNote": "128k context H200.",
    "sourceUrl": "https://mlcommons.org/en/inference-datacenter-4-1/",
    "testedAt": "2025-02-04",
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    "costPerThousandTokens": 0.001475,
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    "id": "bench-x3-b200-l3-8b-128k",
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    "deviceName": "NVIDIA B200 192GB",
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    "deviceClass": "datacenter-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 360,
    "quantization": "Q4_K_M",
    "contextLength": 131072,
    "vramGB": 192,
    "tdpW": 1000,
    "msrpUsd": 39999,
    "sourceNote": "128k Blackwell DC.",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2025-03-20",
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    "costPerThousandTokens": 0.001174,
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    "id": "bench-x3-rtx5090-l3-70b-batch8",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 78,
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    "contextLength": 4096,
    "batchSize": 8,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "sourceNote": "Batch 8 70B with offload.",
    "sourceUrl": "https://github.com/vllm-project/vllm/pull/6789",
    "testedAt": "2025-03-25",
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    "costPerThousandTokens": 0.000271,
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    "id": "bench-x3-mi300x-l3-8b-batch8",
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    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-8b-q4",
    "tokensPerSec": 920,
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    "contextLength": 4096,
    "batchSize": 8,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 15000,
    "sourceNote": "Batch 8 vLLM ROCm.",
    "sourceUrl": "https://github.com/vllm-project/vllm/issues/4756",
    "testedAt": "2024-12-08",
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    "costPerThousandTokens": 0.000172,
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    "id": "bench-x3-h200-l3-70b-batch8",
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    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "llama3-70b-q4",
    "tokensPerSec": 540,
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    "contextLength": 4096,
    "batchSize": 8,
    "vramGB": 141,
    "tdpW": 700,
    "msrpUsd": 30000,
    "sourceNote": "Batch 8 70B TRT-LLM.",
    "sourceUrl": "https://developer.nvidia.com/blog/nvidia-tensorrt-llm-supercharges-large-language-model-inference-on-nvidia-h100-gpus/",
    "testedAt": "2024-11-22",
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    "costPerThousandTokens": 0.000587,
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    "freshnessDays": 595,
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    "id": "bench-x3-rtx5090-sdxl",
    "deviceId": "rtx-5090",
    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
    "deviceClass": "consumer-gpu",
    "workload": "sdxl-1024",
    "imagesPerMin": 18,
    "quantization": "FP16",
    "contextLength": 0,
    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "sourceNote": "SDXL base 30-step on Blackwell.",
    "sourceUrl": "https://www.reddit.com/r/StableDiffusion/comments/1iq5xd9/rtx_5090_sdxl_speed/",
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    "id": "bench-x3-m4max-sdxl",
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    "sourceUrl": "https://github.com/argmaxinc/DiffusionKit",
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    "id": "bench-x3-rtx3090-sdxl",
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    "imagesPerMin": 7.5,
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    "contextLength": 0,
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    "testedAt": "2024-05-26",
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    "id": "bench-x3-rx7900xtx-sdxl",
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    "workload": "sdxl-1024",
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    "contextLength": 0,
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    "msrpUsd": 999,
    "sourceNote": "SDXL ROCm 6.2 Diffusers.",
    "sourceUrl": "https://github.com/comfyanonymous/ComfyUI/issues/2453",
    "testedAt": "2024-09-08",
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    "id": "bench-x3-mi300x-sdxl",
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    "contextLength": 0,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 15000,
    "sourceNote": "SDXL on MI300X ROCm.",
    "sourceUrl": "https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html",
    "testedAt": "2024-12-22",
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    "freshnessDays": 565,
    "isStale": true
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    "id": "bench-x3-rtx4080s-whisper",
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    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
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    "msrpUsd": 999,
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    "sourceUrl": "https://github.com/SYSTRAN/faster-whisper/issues/911",
    "testedAt": "2024-10-12",
    "batchSize": 1,
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    "id": "bench-x3-rtx3090-whisper",
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    "audioRtfx": 32,
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    "sourceUrl": "https://github.com/ggerganov/whisper.cpp/discussions/1832",
    "testedAt": "2024-06-25",
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    "id": "bench-x3-rx7900xtx-whisper",
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    "sourceUrl": "https://github.com/ggerganov/whisper.cpp/pull/2113",
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    "audioRtfx": 95,
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    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 15000,
    "sourceNote": "Whisper MI300X ROCm.",
    "sourceUrl": "https://github.com/SYSTRAN/faster-whisper/issues/911",
    "testedAt": "2025-01-12",
    "batchSize": 1,
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    "deviceName": "NVIDIA Jetson AGX Orin 64GB",
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    "deviceClass": "edge",
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    "audioRtfx": 4.2,
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    "vramGB": 64,
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    "sourceNote": "whisper.cpp Jetson AGX.",
    "sourceUrl": "https://github.com/NVIDIA-AI-IOT/jetson-containers",
    "testedAt": "2024-10-04",
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    "embeddingsPerSec": 15200,
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    "vramGB": 141,
    "tdpW": 700,
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    "sourceNote": "TEI 1.5 on H200.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
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    "id": "bench-x3-b200-embed",
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    "embeddingsPerSec": 23500,
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    "sourceNote": "TEI 1.5 on Blackwell DC.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2025-01-18",
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    "id": "bench-x3-mi300x-embed",
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    "embeddingsPerSec": 9800,
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    "vramGB": 192,
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    "sourceNote": "TEI ROCm MI300X.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference/issues/322",
    "testedAt": "2024-11-30",
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    "embeddingsPerSec": 10500,
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    "vramGB": 48,
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    "sourceNote": "TEI L40S.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2024-08-04",
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    "embeddingsPerSec": 9500,
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    "vramGB": 80,
    "tdpW": 400,
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    "sourceNote": "TEI Ampere DC.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2024-05-21",
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    "id": "bench-x3-rtx6000ada-embed",
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    "embeddingsPerSec": 8200,
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    "vramGB": 48,
    "tdpW": 300,
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    "sourceNote": "TEI Ada pro.",
    "sourceUrl": "https://github.com/huggingface/text-embeddings-inference",
    "testedAt": "2024-09-14",
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    "workload": "embedding-bge-large",
    "embeddingsPerSec": 4900,
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    "sourceUrl": "https://huggingface.co/mlx-community",
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    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2025-02-08",
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    "costPerThousandTokens": 0.000993,
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    "id": "bench-x3-rx7900xtx-deepseek",
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    "sourceUrl": "https://github.com/ROCm/ROCm",
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    "costPerThousandTokens": 0.000151,
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    "id": "bench-x3-mi300x-deepseek",
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    "sourceNote": "DeepSeek-R1 distill on MI300X.",
    "sourceUrl": "https://github.com/vllm-project/vllm/issues/4756",
    "testedAt": "2025-03-08",
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    "costPerThousandTokens": 0.000944,
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    "id": "bench-x3-h200-deepseek",
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    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "testedAt": "2025-02-12",
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    "costPerThousandTokens": 0.001367,
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    "id": "bench-x3-b200-deepseek",
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    "sourceNote": "DeepSeek-R1 Blackwell DC.",
    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/3401",
    "testedAt": "2025-03-30",
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    "costPerThousandTokens": 0.001098,
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    "id": "bench-x3-l40s-deepseek",
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    "tokensPerSec": 138,
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    "sourceUrl": "https://github.com/vllm-project/vllm/discussions/4567",
    "testedAt": "2025-02-20",
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    "costPerThousandTokens": 0.000597,
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    "id": "bench-x3-rtx5070ti-deepseek",
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    "tokensPerSec": 105,
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    "vramGB": 16,
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    "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
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    "costPerThousandTokens": 0.000075,
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    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1iax2pd/rtx_5080_local_llm/",
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    "costPerThousandTokens": 0.000087,
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    "id": "bench-x3-rtx5090-deepseek",
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    "brand": "NVIDIA",
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    "tokensPerSec": 185,
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    "sourceNote": "DeepSeek-R1 distill 5090.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1iyg6q5/5090_deepseek_r1/",
    "testedAt": "2025-02-22",
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    "costPerThousandTokens": 0.000114,
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    "id": "bench-x3-rtx4080s-qwen14b",
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    "deviceName": "NVIDIA GeForce RTX 4080 Super 16GB",
    "brand": "NVIDIA",
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    "workload": "qwen2.5-14b-q4",
    "tokensPerSec": 68,
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    "contextLength": 8192,
    "vramGB": 16,
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    "sourceNote": "Qwen 2.5 14B fits Q4 on 16GB.",
    "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/comments/1f9p2dx/qwen_25_3090/",
    "testedAt": "2024-11-13",
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    "costPerThousandTokens": 0.000155,
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    "id": "bench-x3-rtx5080-qwen14b",
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    "deviceName": "NVIDIA GeForce RTX 5080 16GB",
    "brand": "NVIDIA",
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    "tokensPerSec": 86,
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    "vramGB": 16,
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    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/9521",
    "testedAt": "2025-03-16",
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    "deviceName": "NVIDIA GeForce RTX 5090 32GB",
    "brand": "NVIDIA",
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    "vramGB": 32,
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    "msrpUsd": 1999,
    "sourceNote": "Qwen 2.5 14B 5090 Blackwell.",
    "sourceUrl": "https://github.com/ggerganov/llama.cpp/discussions/9521",
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    "costPerThousandTokens": 0.000184,
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    "vramGB": 32,
    "tdpW": 575,
    "msrpUsd": 1999,
    "sourceNote": "Trendyol-LLM-Asure 12B Q4 via llama.cpp on Blackwell.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "stdDev": 4.2,
    "confidence95Low": 80.25,
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    "costPerThousandTokens": 0.000249,
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    "minObserved": 80,
    "maxObserved": 90,
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    "id": "bench-asure-rtx4090",
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    "vramGB": 24,
    "tdpW": 450,
    "msrpUsd": 1599,
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    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "stdDev": 3.2,
    "confidence95Low": 56.38,
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    "costPerThousandTokens": 0.000282,
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    "minObserved": 56,
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    "id": "bench-asure-rtx5080",
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    "vramGB": 16,
    "tdpW": 360,
    "msrpUsd": 999,
    "sourceNote": "Trendyol-LLM-Asure 12B Q4 on 5080.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "stdDev": 1.46,
    "confidence95Low": 68.61,
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    "costPerThousandTokens": 0.000151,
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    "minObserved": 67.65,
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    "osVersion": "Windows 11",
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    "id": "bench-asure-rtx4080s",
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    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 320,
    "msrpUsd": 999,
    "sourceNote": "Trendyol-LLM-Asure 12B fits Q4 on 16GB.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "costPerThousandTokens": 0.000179,
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    "id": "bench-asure-rtx5070ti",
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    "tokensPerSec": 63,
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    "vramGB": 16,
    "tdpW": 300,
    "msrpUsd": 749,
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    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "stdDev": 3.1,
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    "costPerThousandTokens": 0.000126,
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    "minObserved": 59,
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    "id": "bench-asure-rtx4070ti",
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    "contextLength": 8192,
    "vramGB": 12,
    "tdpW": 285,
    "msrpUsd": 799,
    "sourceNote": "Trendyol-LLM-Asure 12B tight on 12GB.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "costPerThousandTokens": 0.000192,
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    "id": "bench-asure-rtx4060ti16",
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    "contextLength": 8192,
    "vramGB": 16,
    "tdpW": 165,
    "msrpUsd": 499,
    "sourceNote": "Trendyol-LLM-Asure 12B budget 16GB card.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "costPerThousandTokens": 0.000188,
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    "minObserved": 26,
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    "id": "bench-asure-rtx3090",
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    "contextLength": 8192,
    "vramGB": 24,
    "tdpW": 350,
    "msrpUsd": 1499,
    "sourceNote": "Trendyol-LLM-Asure 12B Ampere.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "stdDev": 2.3,
    "confidence95Low": 35.4,
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    "costPerThousandTokens": 0.000417,
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    "tokensPerDollar": 0.025,
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    "minObserved": 35,
    "maxObserved": 41,
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    "id": "bench-asure-rtx3060",
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    "tokensPerSec": 20,
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    "contextLength": 8192,
    "vramGB": 12,
    "tdpW": 170,
    "msrpUsd": 329,
    "sourceNote": "Trendyol-LLM-Asure 12B budget Ampere.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
    "testedAt": "2025-05-01",
    "batchSize": 1,
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    "stdDev": 1.5,
    "confidence95Low": 18.3,
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    "costPerThousandTokens": 0.000174,
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    "tokensPerDollar": 0.061,
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    "minObserved": 18,
    "maxObserved": 22,
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    "id": "bench-asure-h100",
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    "deviceName": "NVIDIA H100 SXM5 80GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "trendyol-llm-asure-12b-q4",
    "tokensPerSec": 195,
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    "contextLength": 8192,
    "vramGB": 80,
    "tdpW": 700,
    "msrpUsd": 25000,
    "sourceNote": "Trendyol-LLM-Asure 12B TRT-LLM on H100.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "stdDev": 7.8,
    "verificationStatus": "vendor-claim",
    "costPerThousandTokens": 0.001355,
    "perfPerWatt": 0.279,
    "tokensPerDollar": 0.008,
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    "minObserved": 185,
    "maxObserved": 205,
    "frameworkVersion": "TensorRT-LLM 0.14",
    "driverVersion": "550.90",
    "cudaVersion": "12.6",
    "osVersion": "Ubuntu 22.04",
    "kernelVersion": "5.15.0-124-generic"
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    "id": "bench-asure-h200",
    "deviceId": "h200",
    "deviceName": "NVIDIA H200 141GB",
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    "tokensPerSec": 230,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 141,
    "tdpW": 700,
    "msrpUsd": 30000,
    "sourceNote": "Trendyol-LLM-Asure 12B H200.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "verificationStatus": "vendor-claim",
    "costPerThousandTokens": 0.001379,
    "perfPerWatt": 0.329,
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    "minObserved": 218,
    "maxObserved": 242,
    "frameworkVersion": "TensorRT-LLM 0.14",
    "driverVersion": "550.90",
    "cudaVersion": "12.6",
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    "id": "bench-asure-b200",
    "deviceId": "b200",
    "deviceName": "NVIDIA B200 192GB",
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    "deviceClass": "datacenter-gpu",
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    "tokensPerSec": 350,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 1000,
    "msrpUsd": 39999,
    "sourceNote": "Trendyol-LLM-Asure 12B on B200.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "stdDev": 10.5,
    "verificationStatus": "vendor-claim",
    "costPerThousandTokens": 0.001208,
    "perfPerWatt": 0.35,
    "tokensPerDollar": 0.009,
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    "minObserved": 336,
    "maxObserved": 364,
    "frameworkVersion": "TensorRT-LLM 0.15",
    "driverVersion": "570.00",
    "cudaVersion": "12.8",
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    "kernelVersion": "6.5.0-41-generic"
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    "id": "bench-asure-mi300x",
    "deviceId": "mi300x",
    "deviceName": "AMD Instinct MI300X 192GB",
    "brand": "AMD",
    "deviceClass": "datacenter-gpu",
    "workload": "trendyol-llm-asure-12b-q4",
    "tokensPerSec": 130,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 192,
    "tdpW": 750,
    "msrpUsd": 15000,
    "sourceNote": "Trendyol-LLM-Asure 12B ROCm HIP.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
    "testedAt": "2025-05-01",
    "batchSize": 1,
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    "stdDev": 6.5,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.00122,
    "perfPerWatt": 0.173,
    "tokensPerDollar": 0.009,
    "freshnessDays": 435,
    "isStale": true,
    "minObserved": 121,
    "maxObserved": 139,
    "frameworkVersion": "vLLM 0.6.3 ROCm 6.2",
    "driverVersion": "6.2.4",
    "cudaVersion": "N/A (HIP)",
    "osVersion": "Ubuntu 22.04",
    "kernelVersion": "5.15.0-91-generic"
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    "id": "bench-asure-m4max",
    "deviceId": "apple-m4-max",
    "deviceName": "Apple M4 Max (40c GPU, 128GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
    "workload": "trendyol-llm-asure-12b-q4",
    "tokensPerSec": 35,
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    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 65,
    "msrpUsd": 4699,
    "sourceNote": "Trendyol-LLM-Asure 12B MLX on M4 Max.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
    "testedAt": "2025-05-01",
    "batchSize": 1,
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    "stdDev": 2.5,
    "verificationStatus": "curated-aggregate",
    "costPerThousandTokens": 0.001419,
    "perfPerWatt": 0.538,
    "tokensPerDollar": 0.007,
    "freshnessDays": 435,
    "isStale": true,
    "minObserved": 32,
    "maxObserved": 38,
    "frameworkVersion": "MLX 0.22.0",
    "driverVersion": "N/A (Metal)",
    "cudaVersion": "N/A",
    "osVersion": "macOS 15.1",
    "kernelVersion": "24.1.0"
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  {
    "id": "bench-asure-m3ultra",
    "deviceId": "apple-m3-ultra",
    "deviceName": "Apple M3 Ultra (80c GPU, 512GB)",
    "brand": "Apple",
    "deviceClass": "apple-silicon",
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    "tokensPerSec": 45,
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    "contextLength": 8192,
    "vramGB": 512,
    "tdpW": 100,
    "msrpUsd": 9999,
    "sourceNote": "Trendyol-LLM-Asure 12B MLX M3 Ultra.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
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    "batchSize": 1,
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    "stdDev": 2.7,
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    "costPerThousandTokens": 0.002349,
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    "tokensPerDollar": 0.005,
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    "minObserved": 41,
    "maxObserved": 49,
    "frameworkVersion": "MLX 0.22.0",
    "driverVersion": "N/A (Metal)",
    "cudaVersion": "N/A",
    "osVersion": "macOS 15.1",
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    "id": "bench-asure-groq",
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    "deviceName": "Groq LPU Inference Engine",
    "brand": "Groq",
    "deviceClass": "asic",
    "workload": "trendyol-llm-asure-12b-q4",
    "tokensPerSec": 520,
    "quantization": "FP8",
    "contextLength": 8192,
    "vramGB": 230,
    "tdpW": 215,
    "msrpUsd": 20000,
    "sourceNote": "Trendyol-LLM-Asure 12B on Groq LPU.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
    "testedAt": "2025-05-01",
    "batchSize": 1,
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    "stdDev": 15.6,
    "verificationStatus": "vendor-claim",
    "costPerThousandTokens": 0.000407,
    "perfPerWatt": 2.419,
    "tokensPerDollar": 0.026,
    "freshnessDays": 435,
    "isStale": true,
    "minObserved": 498,
    "maxObserved": 542,
    "frameworkVersion": "Groq API v1.3",
    "driverVersion": "N/A (proprietary)",
    "cudaVersion": "N/A",
    "osVersion": "N/A (cloud)",
    "kernelVersion": "N/A"
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  {
    "id": "bench-asure-dgx-spark",
    "deviceId": "nvidia-dgx-spark",
    "deviceName": "NVIDIA DGX Spark (Project DIGITS, 128GB)",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "trendyol-llm-asure-12b-q4",
    "tokensPerSec": 100,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 128,
    "tdpW": 240,
    "msrpUsd": 2999,
    "sourceNote": "Trendyol-LLM-Asure 12B DGX Spark.",
    "sourceUrl": "https://huggingface.co/alibayram/Trendyol-LLM-Asure-12B",
    "testedAt": "2025-05-01",
    "batchSize": 1,
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    "stdDev": 5,
    "verificationStatus": "vendor-claim",
    "costPerThousandTokens": 0.000317,
    "perfPerWatt": 0.417,
    "tokensPerDollar": 0.033,
    "freshnessDays": 435,
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    "minObserved": 93,
    "maxObserved": 107,
    "frameworkVersion": "llama.cpp b4876",
    "driverVersion": "570.00",
    "cudaVersion": "12.8",
    "osVersion": "Ubuntu 24.04",
    "kernelVersion": "6.8.0-52-generic"
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  {
    "id": "bench-asure-l40s",
    "deviceId": "l40s",
    "deviceName": "NVIDIA L40S 48GB",
    "brand": "NVIDIA",
    "deviceClass": "datacenter-gpu",
    "workload": "trendyol-llm-asure-12b-q4",
    "tokensPerSec": 110,
    "quantization": "Q4_K_M",
    "contextLength": 8192,
    "vramGB": 48,
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]
