Can I Run DeepSeek-R1-Distill-Qwen-32B on Mac Studio (M4 Max, 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
Yes — DeepSeek R1 Distill Qwen 32B at Q8_0 needs about 36.9 GB of the 48 GB usable on Mac Studio (M4 Max, 64 GB) (~11.1 GB spare), at ~8.3 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~8.3 tok/s
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Mac Studio (M4 Max, 64 GB) — what it gives a model
| Usable memory for models | 48 GB |
| Memory bandwidth | 546 GB/s |
| Form factor | Desktop |
| Operating system | macOS |
| Memory upgradeable | No — soldered |
DeepSeek R1 on Mac Studio (M4 Max, 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 | ~8.3 tok/s | 34 GB |
| Q6_K | 29.2 GB | ✓ Yes | 64K | ~10.6 tok/s | 26.2 GB |
| Q5_K_M | 25.6 GB | ✓ Yes | 64K | ~12.1 tok/s | 22.7 GB |
| Q4_K_M | 22.3 GB | ✓ Yes | 64K | ~14.1 tok/s | 19.3 GB |
| Q3_K_M | 16.6 GB | ✓ Yes | 64K | ~19.2 tok/s | 13.6 GB |
| Q2_K | 13.5 GB | ✓ Yes | 128K | ~24 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 | ~14.1 tok/s |
| DeepSeek R1 Distill Qwen 14B | 10.6 GB | ✓ Fits | ~29.9 tok/s |
| DeepSeek R1 Distill Llama 8B | 6.7 GB | ✓ Fits | ~47.8 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 Studio M4 Max 64 GB limitations
- Memory is soldered; the configuration chosen at purchase is permanent.
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 546 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 Studio (M4 Max, 64 GB)?
Yes — DeepSeek R1 Distill Qwen 32B at Q8_0 needs about 36.9 GB of the 48 GB usable on Mac Studio (M4 Max, 64 GB) (~11.1 GB spare), at ~8.3 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of DeepSeek R1 should I use on Mac Studio (M4 Max, 64 GB)?
Q8_0 — it needs about 36.9 GB of the 48 GB available, downloads as roughly 34 GB, and runs at an estimated 8.3 tokens/sec with up to 32K of context.
What limits DeepSeek R1 on Mac Studio (M4 Max, 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 Studio (M4 Max, 64 GB)
- Devstral on Mac Studio (M4 Max, 64 GB)
- EXAONE 3.5 on Mac Studio (M4 Max, 64 GB)
- Falcon 3 on Mac Studio (M4 Max, 64 GB)
- Gemma 2 Family on Mac Studio (M4 Max, 64 GB)
- Gemma 3 on Mac Studio (M4 Max, 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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