Can I Run DeepSeek-R1-Distill-Qwen-14B on Mac mini (M4, 16 GB)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 20 stycznia 2025
Yes
Yes — DeepSeek R1 Distill Qwen 14B at Q4_K_M needs about 10.6 GB of the 12 GB usable on Mac mini (M4, 16 GB) (~1.4 GB spare), at ~7.1 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q4_K_M · Estimated speed: ~7.1 tok/s
These figures are for DeepSeek-R1-Distill-Qwen-14B, a distill of Qwen2.5-14B — 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.
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Mac mini (M4, 16 GB) — what it gives a model
| Usable memory for models | 12 GB |
| Memory bandwidth | 120 GB/s |
| Form factor | Mini PC |
| Operating system | macOS |
| Memory upgradeable | No — soldered |
| Price | $599 (lib/data/ai-stations.ts (street price), checked 2026-07-06) |
DeepSeek R1 on Mac mini (M4, 16 GB): memory by quantization
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 30.1 GB | ✗ No | — | — | 28 GB |
| Q8_0 | 17 GB | ✗ No | — | — | 14.9 GB |
| Q6_K | 13.6 GB | ✗ No | — | — | 11.5 GB |
| Q5_K_M | 12.1 GB | ✗ No | — | — | 9.9 GB |
| Q4_K_M | 10.6 GB | ✓ Yes | 16K | ~7.1 tok/s | 8.5 GB |
| Q3_K_M | 8.1 GB | ✓ Yes | 16K | ~9.6 tok/s | 6 GB |
| Q2_K | 6.7 GB | ✓ Yes | 32K | ~12 tok/s | 4.6 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 | ✗ Too large | — |
| DeepSeek R1 Distill Qwen 14B | 10.6 GB | ✓ Fits | ~7.1 tok/s |
| DeepSeek R1 Distill Llama 8B | 6.7 GB | ✓ Fits | ~11.8 tok/s |
What to watch out for
- Only ~1.4 GB of headroom at Q4_K_M: a longer context or a second application can push this into swapping.
- 2 larger variants of DeepSeek R1 do 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 16 GB limitations
- The cheapest credible always-on local-AI box, but 16 GB caps it at small and mid-size models.
- Memory is soldered; upgrading means replacing the machine.
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.
- 16 GB unified memory at 120 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, 16 GB)?
Yes — DeepSeek R1 Distill Qwen 14B at Q4_K_M needs about 10.6 GB of the 12 GB usable on Mac mini (M4, 16 GB) (~1.4 GB spare), at ~7.1 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of DeepSeek R1 should I use on Mac mini (M4, 16 GB)?
Q4_K_M — it needs about 10.6 GB of the 12 GB available, downloads as roughly 8.5 GB, and runs at an estimated 7.1 tokens/sec with up to 16K of context.
What limits DeepSeek R1 on Mac mini (M4, 16 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, 16 GB)
- Devstral on Mac mini (M4, 16 GB)
- EXAONE 3.5 on Mac mini (M4, 16 GB)
- Falcon 3 on Mac mini (M4, 16 GB)
- Gemma 2 Family on Mac mini (M4, 16 GB)
- Gemma 3 on Mac mini (M4, 16 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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