Can I Run GPT-OSS on MacBook Pro 16" (M4 Max, 128 GB)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026

Yes — comfortably

Yes, comfortably — GPT-oss 120B at Q3_K_M needs about 52.6 GB of the 96 GB usable on MacBook Pro 16" (M4 Max, 128 GB), leaving ~43.4 GB spare and running at ~5.7 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~5.7 tok/s

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MacBook Pro 16" (M4 Max, 128 GB) — what it gives a model

Usable memory for models	96 GB
Memory bandwidth	546 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered

GPT-OSS on MacBook Pro 16" (M4 Max, 128 GB): memory by quantization

Quant	Memory needed	Fits 96 GB?	Max context	Est. speed	Download
F16	241.4 GB	✗ No	—	—	240 GB
Q8_0	128.9 GB	✗ No	—	—	127.5 GB
Q6_K	99.8 GB	✗ No	—	—	98.4 GB
Q5_K_M	86.5 GB	✓ Yes	64K	~3.5 tok/s	85.1 GB
Q4_K_M	73.9 GB	✓ Yes	64K	~4.1 tok/s	72.5 GB
Q3_K_M	52.6 GB	✓ Yes	64K	~5.7 tok/s	51.2 GB
Q2_K	40.9 GB	✓ Yes	64K	~7.4 tok/s	39.4 GB

Which GPT-OSS sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GPT-oss 120B	73.9 GB	✓ Fits	~4.1 tok/s
GPT-OSS 20B	13.3 GB	✓ Fits	~22.7 tok/s

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.
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 Pro M4 Max 128 GB limitations

Unified memory is soldered — the 128 GB decision is permanent and cannot be upgraded later.
Sustained decode speed on battery is materially below the plugged-in figures quoted here.

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.
128 GB unified memory at 546 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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