Can I Run GPT-OSS on MacBook Pro 16" (M4 Max, 48 GB)?

Written by Jakub Rusinowski · Last updated August 15, 2026

Yes — comfortably

Yes, comfortably — GPT-OSS 20B at Q8_0 needs about 23.4 GB of the 36 GB usable on MacBook Pro 16" (M4 Max, 48 GB), leaving ~12.6 GB spare and running at ~60.8 tok/s (estimated), with room for about 131,072 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~60.8 tok/s

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MacBook Pro 16" (M4 Max, 48 GB) — what it gives a model

Usable memory for models	36 GB
Memory bandwidth	546 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered
Price	$3,999 (lib/data/laptops.ts (street price), checked 2026-07-06)

GPT-OSS on MacBook Pro 16" (M4 Max, 48 GB): memory by quantization

Quant	Memory needed	Fits 36 GB?	Max context	Est. speed	Download
F16	43 GB	✗ No	—	—	41.8 GB
Q8_0	23.4 GB	✓ Yes	128K	~60.8 tok/s	22.2 GB
Q6_K	18.3 GB	✓ Yes	128K	~73.9 tok/s	17.1 GB
Q5_K_M	16 GB	✓ Yes	128K	~82 tok/s	14.8 GB
Q4_K_M	13.8 GB	✓ Yes	128K	~91.4 tok/s	12.6 GB
Q3_K_M	10.1 GB	✓ Yes	128K	~113.6 tok/s	8.9 GB
Q2_K	8.1 GB	✓ Yes	128K	~131.1 tok/s	6.9 GB

Which GPT-OSS sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GPT-OSS 120B	71.9 GB	✗ Too large	—
GPT-OSS 20B	13.8 GB	✓ Fits	~91.4 tok/s

What to watch out for

1 larger variant of GPT-OSS 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 Pro M4 Max 48 GB limitations

Unified memory is soldered and cannot be upgraded after purchase.
macOS reserves a share of unified memory for the system, so not all 48 GB is available to a model.

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.
48 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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