Can I Run GPT-OSS on MacBook Pro 16" (M4 Max, 128 GB)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026
Yes
Yes — GPT-OSS 120B at Q5_K_M needs about 83.9 GB of the 96 GB usable on MacBook Pro 16" (M4 Max, 128 GB) (~12.1 GB spare), at ~63.8 tok/s (estimated), with room for about 131,072 tokens of context.
Confidence: high · Recommended quantization: Q5_K_M · Estimated speed: ~63.8 tok/s
See what else this hardware can run →
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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 | 234.7 GB | ✗ No | — | — | 233.6 GB |
| Q8_0 | 125.2 GB | ✗ No | — | — | 124.1 GB |
| Q6_K | 96.9 GB | ✗ No | — | — | 95.8 GB |
| Q5_K_M | 83.9 GB | ✓ Yes | 128K | ~63.8 tok/s | 82.8 GB |
| Q4_K_M | 71.6 GB | ✓ Yes | 128K | ~72 tok/s | 70.5 GB |
| Q3_K_M | 50.9 GB | ✓ Yes | 128K | ~92.1 tok/s | 49.8 GB |
| Q2_K | 39.5 GB | ✓ Yes | 128K | ~108.7 tok/s | 38.4 GB |
Which GPT-OSS sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| GPT-OSS 120B | 71.6 GB | ✓ Fits | ~72 tok/s |
| GPT-OSS 20B | 13.6 GB | ✓ Fits | ~93.8 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.
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 — GPT-OSS 120B at Q5_K_M needs about 83.9 GB of the 96 GB usable on MacBook Pro 16" (M4 Max, 128 GB) (~12.1 GB spare), at ~63.8 tok/s (estimated), with room for about 131,072 tokens of context.
Which quantization of GPT-OSS should I use on MacBook Pro 16" (M4 Max, 128 GB)?
Q5_K_M — it needs about 83.9 GB of the 96 GB available, downloads as roughly 82.8 GB, and runs at an estimated 63.8 tokens/sec with up to 128K 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
- GPT-OSS on MacBook Pro M4 Max 48 GB
- GPT-OSS on MacBook Pro M4 Pro 24 GB
- GPT-OSS on MacBook Air M4 16 GB
Other Models on MacBook Pro 16" (M4 Max, 128 GB)
- Granite 3.0 on MacBook Pro 16" (M4 Max, 128 GB)
- IBM Granite 4.0 on MacBook Pro 16" (M4 Max, 128 GB)
- IBM Granite 4.1 on MacBook Pro 16" (M4 Max, 128 GB)
- IBM Granite 4.2 on MacBook Pro 16" (M4 Max, 128 GB)
- InternLM 3 on MacBook Pro 16" (M4 Max, 128 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