Can I Run GPT-OSS on MacBook Pro 16" (M4 Max, 128 GB)?
Written by Jakub Rusinowski · Last updated August 15, 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
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.
FAQ
Can I run GPT-OSS on MacBook Pro 16" (M4 Max, 128 GB)?
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.
Which quantization of GPT-OSS should I use on MacBook Pro 16" (M4 Max, 128 GB)?
Q3_K_M — it needs about 52.6 GB of the 96 GB available, downloads as roughly 51.2 GB, and runs at an estimated 5.7 tokens/sec with up to 64K of context.
What limits GPT-OSS on MacBook Pro 16" (M4 Max, 128 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 MacBook Pro 16" (M4 Max, 128 GB)
GPT-OSS on GPUs
What This Model Is Good At
Model & Tools
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