Can I Run DeepSeek-R1-Distill-Qwen-32B on Mac mini (M4 Pro, 64 GB)?
Written by Jakub Rusinowski · Last updated January 20, 2025
These figures are for DeepSeek-R1-Distill-Qwen-32B, a distill of Qwen2.5-32B — not the full DeepSeek R1. The full DeepSeek R1 (671B) needs about 405 GB of weights at Q4_K_M and is a different model.
Yes — comfortably
Yes, comfortably — DeepSeek R1 Distill Qwen 32B at Q6_K needs about 29.2 GB of the 48 GB usable on Mac mini (M4 Pro, 64 GB), leaving ~18.8 GB spare and running at ~5.4 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~5.4 tok/s
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Mac mini (M4 Pro, 64 GB) — what it gives a model
| Usable memory for models | 48 GB |
| Memory bandwidth | 273 GB/s |
| Form factor | Mini PC |
| Operating system | macOS |
| Memory upgradeable | No — soldered |
DeepSeek R1 on Mac mini (M4 Pro, 64 GB): memory by quantization
| Quant | Memory needed | Fits 48 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 66.9 GB | ✗ No | — | — | 64 GB |
| Q8_0 | 36.9 GB | ✓ Yes | 32K | ~4.2 tok/s | 34 GB |
| Q6_K | 29.2 GB | ✓ Yes | 64K | ~5.4 tok/s | 26.2 GB |
| Q5_K_M | 25.6 GB | ✓ Yes | 64K | ~6.2 tok/s | 22.7 GB |
| Q4_K_M | 22.3 GB | ✓ Yes | 64K | ~7.2 tok/s | 19.3 GB |
| Q3_K_M | 16.6 GB | ✓ Yes | 64K | ~9.9 tok/s | 13.6 GB |
| Q2_K | 13.5 GB | ✓ Yes | 128K | ~12.4 tok/s | 10.5 GB |
Which DeepSeek R1 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| DeepSeek R1 (671B) | 406.5 GB | ✗ Too large | — |
| DeepSeek R1 Distill Qwen 32B | 22.3 GB | ✓ Fits | ~7.2 tok/s |
| DeepSeek R1 Distill Qwen 14B | 10.6 GB | ✓ Fits | ~15.6 tok/s |
| DeepSeek R1 Distill Llama 8B | 6.7 GB | ✓ Fits | ~25.7 tok/s |
What to watch out for
- 1 larger variant of DeepSeek R1 does not fit and would need CPU offload or different hardware.
- Memory on this machine is not upgradeable, so the configuration you buy is the ceiling for every model you will ever run on it.
Mac mini M4 Pro 64 GB limitations
- M4 Pro memory bandwidth is half the M4 Max's, so large models that fit will still generate roughly half as fast.
Recommended setup
Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 64 GB unified memory at 273 GB/s, shared between CPU and GPU.
- macOS reserves a share of unified memory for the system, so not all of it is available to a model.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run DeepSeek R1 on Mac mini (M4 Pro, 64 GB)?
Yes, comfortably — DeepSeek R1 Distill Qwen 32B at Q6_K needs about 29.2 GB of the 48 GB usable on Mac mini (M4 Pro, 64 GB), leaving ~18.8 GB spare and running at ~5.4 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of DeepSeek R1 should I use on Mac mini (M4 Pro, 64 GB)?
Q6_K — it needs about 29.2 GB of the 48 GB available, downloads as roughly 26.2 GB, and runs at an estimated 5.4 tokens/sec with up to 64K of context.
What limits DeepSeek R1 on Mac mini (M4 Pro, 64 GB)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput
Other Models on Mac mini (M4 Pro, 64 GB)
- Devstral on Mac mini (M4 Pro, 64 GB)
- EXAONE 3.5 on Mac mini (M4 Pro, 64 GB)
- Falcon 3 on Mac mini (M4 Pro, 64 GB)
- Gemma 2 Family on Mac mini (M4 Pro, 64 GB)
- Gemma 3 on Mac mini (M4 Pro, 64 GB)
DeepSeek R1 on GPUs
- DeepSeek R1 on NVIDIA GeForce RTX 5090
- DeepSeek R1 on NVIDIA GeForce RTX 5080
- DeepSeek R1 on NVIDIA GeForce RTX 5070 Ti
- DeepSeek R1 on NVIDIA GeForce RTX 5070
What This Model Is Good At
Model & Tools
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