Can I Run GPT-OSS 20B on a Apple M1 Max (64GB)?
Runs at full precision (fp16). Zero quality loss.
12 quantizations fit your 64.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 44.0 GB | 45.5 GB | 13.8 GB | +20.0 GB |
| Q8_0 | 23.8 GB | 25.3 GB | 12.1 GB | +40.2 GB |
| Q6_K | 18.7 GB | 20.2 GB | 12.0 GB | +45.3 GB |
| Q5_K_M | 16.3 GB | 17.8 GB | 11.7 GB | +47.7 GB |
| Q5_K_S | 15.8 GB | 17.3 GB | 11.7 GB | +48.2 GB |
| Q4_1 | 14.4 GB | 15.9 GB | 11.6 GB | +49.6 GB |
| Q4_K_M | 14.0 GB | 15.5 GB | 11.6 GB | +50.0 GB |
| Q4_K_S | 13.3 GB | 14.8 GB | 11.6 GB | +50.7 GB |
| Q4_0 | 13.1 GB | 14.6 GB | 11.5 GB | +50.9 GB |
| Q3_K_M | 10.0 GB | 11.5 GB | 11.5 GB | +54.0 GB |
| Q3_K_S | 9.3 GB | 10.8 GB | 11.5 GB | +54.7 GB |
| Q2_K | 8.1 GB | 9.6 GB | 11.5 GB | +55.9 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 64.0GB.
FAQ
Can the Apple M1 Max (64GB) run GPT-OSS 20B?
Yes. The Apple M1 Max (64GB)'s 64.0GB of unified memory is enough to run GPT-OSS 20B at fp16 quantization (44.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 64.0GB. It uses about 44.0GB of memory and 45.5GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.