Can I Run DeepSeek-R1-Distill-Qwen-32B on Mac Studio (M2 Ultra, 192 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 Q8_0 needs about 36.9 GB of the 144 GB usable on Mac Studio (M2 Ultra, 192 GB), leaving ~107.1 GB spare and running at ~12 tok/s (estimated), with room for about 131,072 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~12 tok/s
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Mac Studio (M2 Ultra, 192 GB) — what it gives a model
| Usable memory for models | 144 GB |
| Memory bandwidth | 800 GB/s |
| Form factor | Desktop |
| Operating system | macOS |
| Memory upgradeable | No — soldered |
DeepSeek R1 on Mac Studio (M2 Ultra, 192 GB): memory by quantization
| Quant | Memory needed | Fits 144 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 66.9 GB | ✓ Yes | 128K | ~6.6 tok/s | 64 GB |
| Q8_0 | 36.9 GB | ✓ Yes | 128K | ~12 tok/s | 34 GB |
| Q6_K | 29.2 GB | ✓ Yes | 128K | ~15.3 tok/s | 26.2 GB |
| Q5_K_M | 25.6 GB | ✓ Yes | 128K | ~17.5 tok/s | 22.7 GB |
| Q4_K_M | 22.3 GB | ✓ Yes | 128K | ~20.2 tok/s | 19.3 GB |
| Q3_K_M | 16.6 GB | ✓ Yes | 128K | ~27.4 tok/s | 13.6 GB |
| Q2_K | 13.5 GB | ✓ Yes | 128K | ~34 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 | ~20.2 tok/s |
| DeepSeek R1 Distill Qwen 14B | 10.6 GB | ✓ Fits | ~42 tok/s |
| DeepSeek R1 Distill Llama 8B | 6.7 GB | ✓ Fits | ~65.6 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 M2 Ultra 192 GB limitations
- Superseded by the M3 Ultra, which is why it is often the better used buy for large-model work.
- 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.
- 192 GB unified memory at 800 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 (M2 Ultra, 192 GB)?
Yes, comfortably — DeepSeek R1 Distill Qwen 32B at Q8_0 needs about 36.9 GB of the 144 GB usable on Mac Studio (M2 Ultra, 192 GB), leaving ~107.1 GB spare and running at ~12 tok/s (estimated), with room for about 131,072 tokens of context.
Which quantization of DeepSeek R1 should I use on Mac Studio (M2 Ultra, 192 GB)?
Q8_0 — it needs about 36.9 GB of the 144 GB available, downloads as roughly 34 GB, and runs at an estimated 12 tokens/sec with up to 128K of context.
What limits DeepSeek R1 on Mac Studio (M2 Ultra, 192 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 (M2 Ultra, 192 GB)
- DeepSeek V4 on Mac Studio (M2 Ultra, 192 GB)
- DeepSeek V4.1 on Mac Studio (M2 Ultra, 192 GB)
- Devstral on Mac Studio (M2 Ultra, 192 GB)
- EXAONE 3.5 on Mac Studio (M2 Ultra, 192 GB)
- Falcon 3 on Mac Studio (M2 Ultra, 192 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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