VRAM
24 GB
TDP
355 W
MSRP
$999
Perf/W
0.27 emb/s/W
Cost/1K tok
$0.11/M
Tested
2025-06-09
AMD Radeon RX 7900 XTX 24GB produces approximately 11.0 tok/s on Llama 3 70B at Q4_K_M quantization (batch 1, 4096-token context, 24GB VRAM, 355W TDP). That figure comes from 1 measured run on llama.cpp. The 70B model needs roughly 40GB of VRAM at Q4, so headroom and KV cache budget matter as much as raw throughput.
AMD Radeon RX 7900 XTX 24GB — RDNA 3 consumer flagship. 24GB GDDR6 at ~960 GB/s, 355W TDP. Competes with RTX 4080/4090 on VRAM at dramatically lower price. ROCm support improving but not at CUDA parity.
24GB VRAM at ~$900-1000 makes this the best $/VRAM consumer card if you can tolerate the ROCm ecosystem. Llama 3 8B Q4 runs at ~90+ tok/s via llama.cpp ROCm. Vulkan backends (llama.cpp) are slower but work universally. Best for Linux-native builders who want NVIDIA-tier VRAM at AMD pricing and are comfortable with the software tradeoffs. Not recommended for Windows AI workflows or production CUDA-dependent stacks.
Buy for Linux AI builds where $/VRAM is the priority and you're comfortable with the ROCm ecosystem. Skip for Windows, for production, or if you value plug-and-play over tinkering.
24GB VRAM for Linux-native builders who want NVIDIA-tier capacity at AMD pricing and are comfortable with software tradeoffs
ROCm driver compatibility — pin your kernel and ROCm versions once stable. HIP runtime errors are more common than CUDA errors in edge cases. Vulkan fallback is functional but 30-50% slower.
Ubuntu 22.04/24.04 (good with ROCm), Windows (Vulkan only, slower), macOS (unsupported)
355W TGP. 850W PSU minimum. ROCm power management less mature than NVIDIA. Higher idle power draw than equivalent NVIDIA cards.
AMD Radeon RX 7900 XTX 24GB with 24GB VRAM at 355W TDP, scored across 12 workloads with 19 benchmark records.
Best workload
Embedding throughput
5800 emb/s
Quantization
FP16
1K context · batch 1
| Workload | Score | Quant | Context | σ | Status | Tested |
|---|---|---|---|---|---|---|
| Llama 3 8B Q4 llm | 95.0tok/s | Q4_K_M | 4K | , | Curated Aggregate | 2025-06-09 |
| Llama 3 70B Q4 llm | 24.0tok/s | Q4_K_M | 4K | , | Curated Aggregate | 2025-08-06 |
| SDXL image gen image | 13.0img/min | FP16 | , | , | Curated Aggregate | 2026-03-11 |
| Llama 3 8B FP16 llm | 88.0tok/s | FP16 | 4K | , | Curated Aggregate | 2024-05-18 |
| Qwen 2.5 14B llm | 52.0tok/s | Q4_K_M | 8K | , | Curated Aggregate | 2024-10-11 |
| Whisper transcription audio | 48.0x RT | FP16 | , | , | Curated Aggregate | 2024-09-30 |
| Mistral 7B llm | 95.0tok/s | Q4_K_M | 4K | , | Curated Aggregate | 2025-02-12 |
| Phi-3 Mini llm | 195.0tok/s | Q4_K_M | 4K | , | Curated Aggregate | 2025-03-08 |
| Gemma 2 9B llm | 72.0tok/s | Q4_K_M | 8K | , | Curated Aggregate | 2024-09-05 |
| DeepSeek-R1 7B llm | 78.0tok/s | Q4_K_M | 8K | , | Curated Aggregate | 2025-04-15 |
| Embedding throughput embedding | 5800.0emb/s | FP16 | 1K | , | Curated Aggregate | 2024-11-04 |
| Asure 12B llm | 52.0tok/s | Q4_K_M | 8K | ±3.6 | Curated Aggregate | 2025-05-01 |
AMD Radeon RX 7900 XTX 24GB clears a 9.0/10 based on workload-normalized throughput, memory headroom, efficiency, value, trust, and coverage.
What models fit this 24GB card at different quantization levels.
Trust score
6/10
MyAI rating
9.0
Runs
1
Freshness
Stale
Source-linked row with explicit verification status.
Tested on 2024-11-04; 661 days old.
Measured on your own machine via ollama-local-api on 2026-06-01.
Windows 10 build 26200 + ROCm 7.10 + Ollama 0.24.0 (HIPBLAS). 4 runs per model after a discarded warmup; decode_tps reported as per-run eval_count/eval_duration. Variance <= 0.4% on reproduced models. Captured by site owner on declared rig. Prompt SHA-256: 5e752b688e0761f321a5721d2fa6b57678bf1de1a1180bb4fc9b7e709bafb6a5.
llama-3.2-3b-instruct
3B / Q4_K_M
median 201.87 / min 201.31 / max 202.80 / n=4
qwen-2.5-7b-instruct
7B / Q4_K_M
median 115.44 / min 115.27 / max 116.04 / n=4
mistral-7b-instruct
7B / Q4_K_M
median 118.72 / min 118.38 / max 119.28 / n=4
gemma-4-31b-it
31B / Q4_K_M
n=4, throughput 0 tok/s
Decode failed: model prefilled then produced 0 tokens across all runs on AMD ROCm 7.10 + Ollama 0.24.0. A real, reproducible failure mode, not missing data.
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