Can I Run Qwen3-Coder on MacBook Air (M4, 16 GB)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 29 września 2026

Yes, but it is tight

Yes, but it is tight — Qwen3-Coder 30B-A3B (MoE) at Q2_K needs about 11.6 GB of the 12 GB usable on MacBook Air (M4, 16 GB), leaving only ~0.4 GB before the runtime starts swapping. Expect ~39 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q2_K · Estimated speed: ~39 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

Qwen3-Coder on MacBook Air (M4, 16 GB): memory by quantization

Quant	Memory needed	Fits 12 GB?	Max context	Est. speed	Download
F16	62.6 GB	✗ No	—	—	61 GB
Q8_0	34 GB	✗ No	—	—	32.4 GB
Q6_K	26.6 GB	✗ No	—	—	25 GB
Q5_K_M	23.2 GB	✗ No	—	—	21.6 GB
Q4_K_M	20 GB	✗ No	—	—	18.4 GB
Q3_K_M	14.6 GB	✗ No	—	—	13 GB
Q2_K	11.6 GB	✓ Yes	8K	~39 tok/s	10 GB

Which Qwen3-Coder sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen3-Coder 480B-A35B (MoE)	291.6 GB	✗ Too large	—
Qwen3-Coder-Next (80B-A3B MoE)	49.4 GB	✗ Too large	—
Qwen3-Coder 30B-A3B (MoE)	20 GB	✗ Too large	—

What to watch out for

Only ~0.4 GB of headroom at Q2_K: a longer context or a second application can push this into swapping.
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.
3 larger variants of Qwen3-Coder 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.

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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