deepseek-r1-7b vs qwen2.5-14b on Apple M2 Pro
Compare deepseek-r1-7b and qwen2.5-14b running locally on Apple M2 Pro (16GB unified) — tokens/sec, VRAM fit, and quality scores for local inference.
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| Quant | Q4_K_M |
| VRAM required | 4.7GB |
| Fits on Apple M2 Pro | ✓ |
| Quality score | 93 / 100 |
| Eval sources | GPQA Diamond · MATH-500 · AIME 2024 · Codeforces · LiveCodeBench |
| Data | estimate |
| Quant | Q4_K_M |
| VRAM required | 9GB |
| Fits on Apple M2 Pro | ✓ |
| Quality score | 61 / 100 |
| Eval sources | MMLU-Pro · IFEval · BBH · LiveCodeBench |
| Data | Community benchmark |
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Which is faster on Apple M2 Pro: deepseek-r1-7b or qwen2.5-14b?
deepseek-r1-7b is faster on Apple M2 Pro in this dataset (46 vs 22 tok/s decode).
Do both models fit on Apple M2 Pro?
deepseek-r1-7b: ✓. qwen2.5-14b: ✓. The fit labels use Q4_K_M VRAM estimates and the benchmark data available for this GPU.
How much VRAM do these models need?
deepseek-r1-7b needs about 4.7GB in Q4_K_M; qwen2.5-14b needs about 9GB 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.