llama3.1-8b vs mistral-24b on RTX 4090
Compare llama3.1-8b and mistral-24b 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 | 5GB |
| Fits on RTX 4090 | ✓ |
| Quality score | 48 / 100 |
| Eval sources | MMLU · MMLU-Pro · IFEval · GSM8K · ARC-C · HumanEval · MBPP+ |
| Data | Community benchmark |
| Quant | Q4_K_M |
| VRAM required | 14.4GB |
| Fits on RTX 4090 | ✓ |
| Quality score | 66 / 100 |
| Eval sources | MMLU-Pro · IFEval · MT-Bench · HumanEval |
| Data | Community benchmark |
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Which is faster on RTX 4090: llama3.1-8b or mistral-24b?
llama3.1-8b is faster on RTX 4090 in this dataset (148 vs 58 tok/s decode).
Do both models fit on RTX 4090?
llama3.1-8b: ✓. mistral-24b: ✓. The fit labels use Q4_K_M VRAM estimates and the benchmark data available for this GPU.
How much VRAM do these models need?
llama3.1-8b needs about 5GB in Q4_K_M; mistral-24b needs about 14.4GB 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.