Can I Run GPT-OSS on Mac mini (M4 Pro, 64 GB)?

作者： Jakub Rusinowski · 最后更新： 2026年8月15日

Yes — comfortably

Yes, comfortably — GPT-OSS 20B at Q8_0 needs about 23.4 GB of the 48 GB usable on Mac mini (M4 Pro, 64 GB), leaving ~24.6 GB spare and running at ~33.4 tok/s (estimated), with room for about 131,072 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~33.4 tok/s

Buy This HardwareAMD Radeon RX 7900 XTX 24GB — 24 GB VRAM · 355 W board power Deploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

Mac mini (M4 Pro, 64 GB) — what it gives a model

Usable memory for models	48 GB
Memory bandwidth	273 GB/s
Form factor	Mini PC
Operating system	macOS
Memory upgradeable	No — soldered

GPT-OSS on Mac mini (M4 Pro, 64 GB): memory by quantization

Quant	Memory needed	Fits 48 GB?	Max context	Est. speed	Download
F16	43 GB	✓ Yes	64K	~19.1 tok/s	41.8 GB
Q8_0	23.4 GB	✓ Yes	128K	~33.4 tok/s	22.2 GB
Q6_K	18.3 GB	✓ Yes	128K	~41.5 tok/s	17.1 GB
Q5_K_M	16 GB	✓ Yes	128K	~46.7 tok/s	14.8 GB
Q4_K_M	13.8 GB	✓ Yes	128K	~53 tok/s	12.6 GB
Q3_K_M	10.1 GB	✓ Yes	128K	~68.5 tok/s	8.9 GB
Q2_K	8.1 GB	✓ Yes	128K	~81.6 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	~53 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.

Mac mini M4 Pro 64 GB limitations

M4 Pro memory bandwidth is half the M4 Max's, so large models that fit will still generate roughly half as fast.

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.
64 GB unified memory at 273 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.

FAQ