llama3.1-70b vs mistral-7b on RTX 4090
Compare llama3.1-70b and mistral-7b running locally on RTX 4090 (24GB VRAM) — tokens/sec, VRAM fit, and quality scores for local inference.
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| Quant | Q4_K_M |
| VRAM required | 42GB |
| Fits on RTX 4090 | ✗ No |
| Quality score | 66 / 100 |
| Eval sources | MMLU · MMLU-Pro · IFEval · GSM8K · ARC-C · HumanEval · MBPP+ |
| Data | estimate |
| Quant | Q4_K_M |
| VRAM required | 4.7GB |
| Fits on RTX 4090 | ✓ |
| Quality score | 65 / 100 |
| Eval sources | MMLU · GSM8K · ARC-C · TruthfulQA · HumanEval |
| Data | Community benchmark |
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Which is faster on RTX 4090: llama3.1-70b or mistral-7b?
This RTX 4090 comparison shows VRAM fit and quality data for llama3.1-70b vs mistral-7b, but one or both decode-speed benchmarks are still missing.
Do both models fit on RTX 4090?
llama3.1-70b: ✗ No. mistral-7b: ✓. The fit labels use Q4_K_M VRAM estimates and the benchmark data available for this GPU.
How much VRAM do these models need?
llama3.1-70b needs about 42GB in Q4_K_M; mistral-7b needs about 4.7GB in Q4_K_M.
Why can speed differ by GPU?
Local LLM speed depends on GPU memory bandwidth, backend, quantization, context length, and whether the model fits entirely in VRAM or unified memory.
How can I correct this comparison?
Use the GitHub correction link on this page with your GPU, model, quantization, context length, and measured decode tokens per second.