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

Superseded model. Codestral has been superseded by Devstral. This page is kept for reference; the newer family is a better starting point. View Devstral →

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 29 maja 2024

Yes

Yes — Codestral 22B at Q2_K needs about 10 GB of the 12 GB usable on MacBook Air (M4, 16 GB) (~2 GB spare), at ~7.8 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q2_K · Estimated speed: ~7.8 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

Codestral on MacBook Air (M4, 16 GB): memory by quantization

Quant	Memory needed	Fits 12 GB?	Max context	Est. speed	Download
F16	47.1 GB	✗ No	—	—	44.4 GB
Q8_0	26.3 GB	✗ No	—	—	23.6 GB
Q6_K	20.9 GB	✗ No	—	—	18.2 GB
Q5_K_M	18.4 GB	✗ No	—	—	15.7 GB
Q4_K_M	16.1 GB	✗ No	—	—	13.4 GB
Q3_K_M	12.1 GB	✗ No	—	—	9.5 GB
Q2_K	10 GB	✓ Yes	16K	~7.8 tok/s	7.3 GB

What to watch out for

Q2_K 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.
1 larger variant of Codestral 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.

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 computed from this model's published attention configuration.

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