Can I Run Devstral on MacBook Air (M4, 16 GB)?

作者： Jakub Rusinowski · 最后更新： 2026年8月15日

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 MacBook Air (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

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MacBook Air (M4, 16 GB) — what it gives a model

Usable memory for models	12 GB
Memory bandwidth	120 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered

Devstral on MacBook Air (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.

MacBook Air M4 16 GB limitations

Fanless: sustained generation throttles on long runs in a way the same chip in a MacBook Pro does not.
16 GB unified memory is shared with the OS and every open app.

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.

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