mistral-24b vs qwen2.5-7b on RTX 3080
Compare mistral-24b and qwen2.5-7b running locally on RTX 3080 (10GB VRAM) — tokens/sec, VRAM fit, and quality scores for local inference.
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| Quant | Q4_K_M |
| VRAM required | 14.4GB |
| Fits on RTX 3080 | ✗ No |
| Quality score | 66 / 100 |
| Eval sources | MMLU-Pro · IFEval · MT-Bench · HumanEval |
| Data | estimate |
| Quant | Q4_K_M |
| VRAM required | 4.7GB |
| Fits on RTX 3080 | ✓ |
| Quality score | 56 / 100 |
| Eval sources | MMLU-Pro · IFEval · BBH · LiveCodeBench |
| Data | Community benchmark |
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Which is faster on RTX 3080: mistral-24b or qwen2.5-7b?
This RTX 3080 comparison shows VRAM fit and quality data for mistral-24b vs qwen2.5-7b, but one or both decode-speed benchmarks are still missing.
Do both models fit on RTX 3080?
mistral-24b: ✗ No. qwen2.5-7b: ✓. The fit labels use Q4_K_M VRAM estimates and the benchmark data available for this GPU.
How much VRAM do these models need?
mistral-24b needs about 14.4GB in Q4_K_M; qwen2.5-7b 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.