Can I Run GPT-OSS on MacBook Air (M4, 16 GB)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026

Yes

Yes — GPT-OSS 20B at Q3_K_M needs about 9.7 GB of the 12 GB usable on MacBook Air (M4, 16 GB) (~2.3 GB spare), at ~7.4 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~7.4 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

GPT-OSS on MacBook Air (M4, 16 GB): memory by quantization

Quant	Memory needed	Fits 12 GB?	Max context	Est. speed	Download
F16	41.2 GB	✗ No	—	—	40 GB
Q8_0	22.5 GB	✗ No	—	—	21.3 GB
Q6_K	17.6 GB	✗ No	—	—	16.4 GB
Q5_K_M	15.4 GB	✗ No	—	—	14.2 GB
Q4_K_M	13.3 GB	✗ No	—	—	12.1 GB
Q3_K_M	9.7 GB	✓ Yes	32K	~7.4 tok/s	8.5 GB
Q2_K	7.8 GB	✓ Yes	64K	~9.4 tok/s	6.6 GB

Which GPT-OSS sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GPT-oss 120B	73.9 GB	✗ Too large	—
GPT-OSS 20B	13.3 GB	✗ Too large	—

What to watch out for

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.
2 larger variants of GPT-OSS 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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