Can I Run Devstral on Mac mini (M4, 16 GB)?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes, but it is tight
Yes, but it is tight — Devstral-2 22B at Q3_K_M needs about 11.8 GB of the 12 GB usable on Mac mini (M4, 16 GB), leaving only ~0.2 GB before the runtime starts swapping. Expect ~6.3 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: low · Recommended quantization: Q3_K_M · Estimated speed: ~6.3 tok/s
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) |
Devstral on Mac mini (M4, 16 GB): memory by quantization
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|
| F16 | 46.4 GB | ✗ No | — | — | 44 GB |
| Q8_0 | 25.8 GB | ✗ No | — | — | 23.4 GB |
| Q6_K | 20.5 GB | ✗ No | — | — | 18 GB |
| Q5_K_M | 18 GB | ✗ No | — | — | 15.6 GB |
| Q4_K_M | 15.7 GB | ✗ No | — | — | 13.3 GB |
| Q3_K_M | 11.8 GB | ✓ Yes | 8K | ~6.3 tok/s | 9.4 GB |
| Q2_K | 9.6 GB | ✓ Yes | 16K | ~8 tok/s | 7.2 GB |
Which Devstral sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|
| Devstral-2 123B | 77.9 GB | ✗ Too large | — |
| Devstral Small 24B | 16.6 GB | ✗ Too large | — |
| Devstral-2 22B | 15.7 GB | ✗ Too large | — |
What to watch out for
- Only ~0.2 GB of headroom at Q3_K_M: a longer context or a second application can push this into swapping.
- Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
- 3 larger variants of Devstral do not fit and would need CPU offload or different hardware.
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
- 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 inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run Devstral on Mac mini (M4, 16 GB)?
Yes, but it is tight — Devstral-2 22B at Q3_K_M needs about 11.8 GB of the 12 GB usable on Mac mini (M4, 16 GB), leaving only ~0.2 GB before the runtime starts swapping. Expect ~6.3 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Devstral should I use on Mac mini (M4, 16 GB)?
Q3_K_M — it needs about 11.8 GB of the 12 GB available, downloads as roughly 9.4 GB, and runs at an estimated 6.3 tokens/sec with up to 8K of context.
What limits Devstral 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 Computers
Other Models on Mac mini (M4, 16 GB)
Devstral on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | Devstral model page | Check your hardware