deepseek-r1-7b vs qwen2.5-32b on Apple M2 Pro
Compare deepseek-r1-7b and qwen2.5-32b 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 | 19.5GB |
| Fits on Apple M2 Pro | ✗ No |
| Quality score | 69 / 100 |
| Eval sources | MMLU-Pro · IFEval · BBH · LiveCodeBench |
| Data | estimate |
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Which is faster on Apple M2 Pro: deepseek-r1-7b or qwen2.5-32b?
This Apple M2 Pro comparison shows VRAM fit and quality data for deepseek-r1-7b vs qwen2.5-32b, but one or both decode-speed benchmarks are still missing.
Do both models fit on Apple M2 Pro?
deepseek-r1-7b: ✓. qwen2.5-32b: ✗ No. 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; qwen2.5-32b needs about 19.5GB 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.