Can I Run GPT-OSS on MacBook Air (M4, 16 GB)?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes
Yes — GPT-OSS 20B at Q3_K_M needs about 9.7 GB of the 12 GB usable on MacBook Air (M4, 16 GB) (~2.3 GB spare), at ~7.4 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~7.4 tok/s
MacBook Air (M4, 16 GB) — what it gives a model
| Usable memory for models | 12 GB |
| Memory bandwidth | 120 GB/s |
| Form factor | Laptop |
| Operating system | macOS |
| Memory upgradeable | No — soldered |
GPT-OSS on MacBook Air (M4, 16 GB): memory by quantization
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|
| F16 | 41.2 GB | ✗ No | — | — | 40 GB |
| Q8_0 | 22.5 GB | ✗ No | — | — | 21.3 GB |
| Q6_K | 17.6 GB | ✗ No | — | — | 16.4 GB |
| Q5_K_M | 15.4 GB | ✗ No | — | — | 14.2 GB |
| Q4_K_M | 13.3 GB | ✗ No | — | — | 12.1 GB |
| Q3_K_M | 9.7 GB | ✓ Yes | 32K | ~7.4 tok/s | 8.5 GB |
| Q2_K | 7.8 GB | ✓ Yes | 64K | ~9.4 tok/s | 6.6 GB |
Which GPT-OSS sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|
| GPT-oss 120B | 73.9 GB | ✗ Too large | — |
| GPT-OSS 20B | 13.3 GB | ✗ Too large | — |
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.
- 2 larger variants of GPT-OSS do 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.
MacBook Air M4 16 GB limitations
- Fanless: sustained generation throttles on long runs in a way the same chip in a MacBook Pro does not.
- 16 GB unified memory is shared with the OS and every open app.
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.
- 16 GB unified memory at 120 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 Air (M4, 16 GB)?
Yes — GPT-OSS 20B at Q3_K_M needs about 9.7 GB of the 12 GB usable on MacBook Air (M4, 16 GB) (~2.3 GB spare), at ~7.4 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of GPT-OSS should I use on MacBook Air (M4, 16 GB)?
Q3_K_M — it needs about 9.7 GB of the 12 GB available, downloads as roughly 8.5 GB, and runs at an estimated 7.4 tokens/sec with up to 32K of context.
What limits GPT-OSS on MacBook Air (M4, 16 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 Air (M4, 16 GB)
GPT-OSS on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | GPT-OSS model page | Check your hardware