Can I Run GPT-OSS on Mac Studio (M3 Ultra, 256 GB)?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes — comfortably
Yes, comfortably — GPT-OSS 120B at Q8_0 needs about 125.5 GB of the 192 GB usable on Mac Studio (M3 Ultra, 256 GB), leaving ~66.5 GB spare and running at ~63.5 tok/s (estimated), with room for about 131,072 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~63.5 tok/s
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Mac Studio (M3 Ultra, 256 GB) — what it gives a model
| Usable memory for models | 192 GB |
| Memory bandwidth | 819 GB/s |
| Form factor | Desktop |
| Operating system | macOS |
| Memory upgradeable | No — soldered |
GPT-OSS on Mac Studio (M3 Ultra, 256 GB): memory by quantization
| Quant | Memory needed | Fits 192 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 235 GB | ✗ No | — | — | 233.6 GB |
| Q8_0 | 125.5 GB | ✓ Yes | 128K | ~63.5 tok/s | 124.1 GB |
| Q6_K | 97.2 GB | ✓ Yes | 128K | ~77 tok/s | 95.8 GB |
| Q5_K_M | 84.2 GB | ✓ Yes | 128K | ~85.3 tok/s | 82.8 GB |
| Q4_K_M | 71.9 GB | ✓ Yes | 128K | ~94.9 tok/s | 70.5 GB |
| Q3_K_M | 51.2 GB | ✓ Yes | 128K | ~117.4 tok/s | 49.8 GB |
| Q2_K | 39.8 GB | ✓ Yes | 128K | ~135 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 | ~94.9 tok/s |
| GPT-OSS 20B | 13.8 GB | ✓ Fits | ~120.6 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 M3 Ultra 256 GB limitations
- The largest single-box unified memory pool available, but at roughly half the memory bandwidth of a high-end discrete GPU — very large models load, then run slowly.
- 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.
- 256 GB unified memory at 819 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
Can I run GPT-OSS on Mac Studio (M3 Ultra, 256 GB)?
Yes, comfortably — GPT-OSS 120B at Q8_0 needs about 125.5 GB of the 192 GB usable on Mac Studio (M3 Ultra, 256 GB), leaving ~66.5 GB spare and running at ~63.5 tok/s (estimated), with room for about 131,072 tokens of context.
Which quantization of GPT-OSS should I use on Mac Studio (M3 Ultra, 256 GB)?
Q8_0 — it needs about 125.5 GB of the 192 GB available, downloads as roughly 124.1 GB, and runs at an estimated 63.5 tokens/sec with up to 128K of context.
What limits GPT-OSS on Mac Studio (M3 Ultra, 256 GB)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput
Other Computers
Other Models on Mac Studio (M3 Ultra, 256 GB)
- Granite 3.0 on Mac Studio (M3 Ultra, 256 GB)
- IBM Granite 4.0 on Mac Studio (M3 Ultra, 256 GB)
- IBM Granite 4.1 on Mac Studio (M3 Ultra, 256 GB)
- IBM Granite 4.2 on Mac Studio (M3 Ultra, 256 GB)
- InternLM 3 on Mac Studio (M3 Ultra, 256 GB)
GPT-OSS on GPUs
- GPT-OSS on NVIDIA GeForce RTX 5080
- GPT-OSS on NVIDIA GeForce RTX 5070 Ti
- GPT-OSS on NVIDIA GeForce RTX 5070
- GPT-OSS on NVIDIA GeForce RTX 5060 Ti 16GB
What This Model Is Good At
- Best local LLMs for general assistant
- Best local LLMs for document analysis
- Best local LLMs for enterprise assistant