Can I Run GPT-OSS on Mac Studio (M2 Ultra, 192 GB)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026

Yes

Yes — GPT-oss 120B at Q8_0 needs about 125.5 GB of the 144 GB usable on Mac Studio (M2 Ultra, 192 GB) (~18.5 GB spare), at ~62.3 tok/s (estimated), with room for about 131,072 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~62.3 tok/s

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Mac Studio (M2 Ultra, 192 GB) — what it gives a model

Usable memory for models	144 GB
Memory bandwidth	800 GB/s
Form factor	Desktop
Operating system	macOS
Memory upgradeable	No — soldered

GPT-OSS on Mac Studio (M2 Ultra, 192 GB): memory by quantization

Quant	Memory needed	Fits 144 GB?	Max context	Est. speed	Download
F16	235 GB	✗ No	—	—	233.6 GB
Q8_0	125.5 GB	✓ Yes	128K	~62.3 tok/s	124.1 GB
Q6_K	97.2 GB	✓ Yes	128K	~75.6 tok/s	95.8 GB
Q5_K_M	84.2 GB	✓ Yes	128K	~83.8 tok/s	82.8 GB
Q4_K_M	71.9 GB	✓ Yes	128K	~93.4 tok/s	70.5 GB
Q3_K_M	51.2 GB	✓ Yes	128K	~115.7 tok/s	49.8 GB
Q2_K	39.8 GB	✓ Yes	128K	~133.1 tok/s	38.4 GB

Which GPT-OSS sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GPT-oss 120B	71.9 GB	✓ Fits	~93.4 tok/s
GPT-OSS 20B	13.8 GB	✓ Fits	~118.8 tok/s

What to watch out for

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 Studio M2 Ultra 192 GB limitations

Superseded by the M3 Ultra, which is why it is often the better used buy for large-model work.
Memory is soldered; the configuration chosen at purchase is permanent.

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.
192 GB unified memory at 800 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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