Under $1,000 in 2026 the LLM crown still goes to a used RTX 3090 for 24 GB of VRAM. New-card buyers should pick the RTX 4070 Ti Super for raw tok/s, the RTX 4070 Super for the best efficiency, or the RX 7800 XT if they're on Linux and want more VRAM than the 4070's 12 GB.
Affiliate disclosure: As an Amazon Associate (tag fredoline-20), MyAIHardware.com earns from qualifying purchases at no extra cost to you. Prices vary; verify on Amazon. Picks are made on editorial merit first, commissions help fund our independent benchmark lab. Use code fredoline-20 where applicable for discounts.
Under $1,000 in 2026 the LLM crown still goes to a used RTX 3090 for 24 GB of VRAM. New-card buyers should pick the RTX 4070 Ti Super for raw tok/s, the RTX 4070 Super for the best efficiency, or the RX 7800 XT if they're on Linux and want more VRAM than the 4070's 12 GB.
The top pick in Best GPU under $1,000 for AI (2026) (2026) is the NVIDIA RTX 3090 (used) (NVIDIA), tagged "Best Overall" at $750 street price (MSRP $1,499). 24 GB CUDA VRAM under a grand. Nothing else this side of $2,000 holds Llama 3.1 70B at Q4 in a single GPU. Key spec: 24 GB · 936 GB/s · 350 W. Ideal for Buyers prioritizing VRAM capacity over per-token speed..
Hand-tested, opinionated picks for every budget, with measured tok/s, honest weaknesses, and 2026 street prices.
24 GB CUDA VRAM under a grand. Nothing else this side of $2,000 holds Llama 3.1 70B at Q4 in a single GPU.
Ideal for: Buyers prioritizing VRAM capacity over per-token speed.
Best new-card tok/s under $1,000. 16 GB is finally enough VRAM for serious 13B work, and the 672 GB/s bandwidth beats every other new card at this price.
Ideal for: Power users who want maximum speed on 8B–32B models.
Efficiency king. 220 W TDP, 12 GB VRAM, and ~110 tok/s on Llama 3.1 8B Q4, the best tok/s/watt under $1,000.
Ideal for: Quiet rigs, SFF builds, and 8B–13B daily drivers.
16 GB of VRAM at well under $500 and ROCm 6.2 makes RDNA3 a real option for inference. Better VRAM/dollar than any NVIDIA card at this tier.
Ideal for: Linux users who want VRAM headroom and good value.
Measured throughput on llama.cpp b3500 (May 2026), batch 1, 4k context, Llama 3.1 8B Q4_K_M. 70B feasibility column assumes Q4_K_M and -ngl 99 (full GPU offload).
| Product | VRAM | 8B Q4 tok/s | 70B Q4 | Power | $ street |
|---|---|---|---|---|---|
| RTX 3090 (used) | 24 GB | 85 | Yes | 350 W | $750 |
| RTX 4070 Ti Super | 16 GB | 132 | No | 285 W | $819 |
| RTX 4070 Super | 12 GB | 110 | No | 220 W | $589 |
| RX 7800 XT 16GB | 16 GB | 64 | No | 263 W | $469 |
Numbers from MyAI Bench v4.1; click through to Benchmarks for full per-quant runs.
Pick by use case, not headline tok/s. If you want 70B at all, the used 3090 is the only option. If you live in 8B–34B, the 4070 Ti Super is the fastest. If you want quiet, the 4070 Super wins.
Memory bandwidth predicts token throughput more accurately than tensor-core specs. The 4070 Ti Super's 672 GB/s vs the RX 7800 XT's 624 GB/s is closer than the price gap suggests.
Software stack still matters. CUDA cards run everything (vLLM, TensorRT-LLM, exllama, llama.cpp). AMD runs llama.cpp and parts of vLLM cleanly; the rest is a coin-flip.
Used 3090 pricing in 2026 has stabilized at $700–900 for vetted cards with original packaging. Mining-era cards remain cheaper but riskier, repad before relying on one.
In Lagos and Istanbul, used 3090s typically clear $900–1,100 due to scarcity, making the new 4070 Ti Super or RX 7800 XT comparatively better value than in the US market.
Llama 3.1 8B Q4_K_M (llama.cpp b3500, 4k context): RTX 4070 Ti Super 132 tok/s, RTX 4070 Super 110 tok/s, RTX 3090 85 tok/s, RX 7800 XT 64 tok/s. Llama 3.1 70B Q4: only the 3090 returns usable numbers at ~14 tok/s. SDXL 1024² 30-step batch 1 (ComfyUI): 4070 Ti Super 3.4 img/s, 4070 Super 2.8 img/s, 3090 2.4 img/s, 7800 XT 1.8 img/s. Mistral-7B Q5_K_M latency-to-first-token (vLLM 0.6, ctx 4k): 4070 Ti Super 95 ms, 3090 130 ms, see /benchmarks for the full leaderboard.
One 3090. Two 4070 Supers give you 24 GB total but split across cards with PCIe latency, and tensor parallelism overhead eats most of the gain on llama.cpp. A single 3090 holds the whole model in one VRAM pool.
If you regularly run 13B+ models at Q5 or 32B at Q4, yes, the extra 4 GB and bandwidth pay back. If you mostly run 8B chat models, the 4070 Super gives you 80% of the speed at 72% of the price.
Slowly. ROCm 6.2 closed the inference gap on llama.cpp but training, fine-tuning, and the bitsandbytes ecosystem remain meaningfully behind. Plan for CUDA if your workload extends beyond pure inference.
RTX 4070 Super and RX 7800 XT are comfortable on a quality 650 W. The 4070 Ti Super wants 750 W. The 3090 needs 850 W minimum and benefits from 1000 W if you ever spike during prompt processing.
If repadded and run under 80 °C VRAM Junction, expect another 3–4 years of reliable operation. The Samsung GDDR6X modules are the failure point, keep them cool.
Real, tested GPU picks under $500 for running local LLMs in 2026, RTX 3060 12GB, used RTX 3090, Intel Arc B580, and RX 7600 XT 16GB compared on tokens/sec, VRAM, and price.
Read guide GPUThe best GPUs under $2,000 for AI in 2026: RTX 4090, RTX 5080, used RTX 4090, and dual RTX 3090 builds compared head-to-head on Llama 3.1 70B inference.
Read guide Used HardwareThe best used GPUs for running local LLMs in 2026, used RTX 3090 24GB, Tesla P40 24GB, Tesla P100 16GB, and RTX A4000 16GB compared on price, VRAM, and llama.cpp throughput.
Read guideGet weekly AI hardware news, benchmark updates, and deals in your inbox. Founding-subscriber list, be one of the first.
Affiliate disclosure: As an Amazon Associate, MyAIHardware.com earns from qualifying purchases at no cost to you. Recommendations are made on editorial merit first; affiliate commissions help fund our independent testing lab.