Can the Apple M1 (16GB) run gpt-oss 20B?
- FP16
- Q8_0
- Q6_K
- Q5_K_M
- Q4_K_M
- Q3_K_M
- Q2_K
Only just. gpt-oss 20B fits on the Apple M1 (16GB) at Q2_K, which costs noticeable quality. Expect about 30 tokens/second. A card with more memory is a better experience.
Run it with Ollama
Ollama's default gpt-oss:20b download is Q4_K_M, which needs about 13.7 GB, more than the 10.7 GB available. Download the Q2_K GGUF instead (LM Studio lists quants per model), or try a smaller context length.
Run it in the cloud
Want full quality? Run gpt-oss 20B on a bigger rented GPU.
Rent a cloud GPU by the hour. No upfront cost, stop any time.
Memory needed by quantization
At 8,192 tokens of context. The black line marks the 10.7 GB the Apple M1 (16GB) can use. macOS lets the GPU use about 10.7 GB of the 16 GB unified memory by default.
| Quant | Quality | Memory | Fits? | Speed | |
|---|---|---|---|---|---|
| FP16 | Full quality | 42.8 GB | Too big | — | |
| Q8_0 | Near-lossless | 23.2 GB | Too big | — | |
| Q6_K | Excellent | 18.1 GB | Too big | — | |
| Q5_K_M | Very good | 15.9 GB | Too big | — | |
| Q4_K_M | Good, most popular | 13.7 GB | Too big | — | |
| Q3_K_M | Noticeably degraded | 11.2 GB | Too big | — | |
| Q2_K | Heavily degraded | 8.8 GB | Fits | ~30 tok/s |
GPUs that run gpt-oss 20B comfortably
Models that run well on the Apple M1 (16GB)
Frequently asked questions
Can Apple M1 (16GB) run gpt-oss 20B?
Only just. gpt-oss 20B fits on the Apple M1 (16GB) at Q2_K, which costs noticeable quality. Expect about 30 tokens/second. A card with more memory is a better experience.
How much VRAM does gpt-oss 20B need?
About 13.7 GB at Q4_K_M with 8,192 tokens of context, and about 42.8 GB at full FP16 precision.
How fast is gpt-oss 20B on the Apple M1 (16GB)?
Roughly 30 tokens per second at Q2_K, estimated from the Apple M1 (16GB)'s 68 GB/s memory bandwidth.