Can I Run GPT-OSS 20B on a Apple M3 (16GB)?
Runs at Q5_K_S — good quality with reasonable headroom.
8 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_SBEST | 15.8 GB | 17.3 GB | 11.7 GB | +0.2 GB |
| Q4_1 | 14.4 GB | 15.9 GB | 11.6 GB | +1.6 GB |
| Q4_K_M | 14.0 GB | 15.5 GB | 11.6 GB | +2.0 GB |
| Q4_K_S | 13.3 GB | 14.8 GB | 11.6 GB | +2.7 GB |
| Q4_0 | 13.1 GB | 14.6 GB | 11.5 GB | +2.9 GB |
| Q3_K_M | 10.0 GB | 11.5 GB | 11.5 GB | +6.0 GB |
| Q3_K_S | 9.3 GB | 10.8 GB | 11.5 GB | +6.7 GB |
| Q2_K | 8.1 GB | 9.6 GB | 11.5 GB | +7.9 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 16.0GB.
FAQ
Can the Apple M3 (16GB) run GPT-OSS 20B?
Yes. The Apple M3 (16GB)'s 16.0GB of unified memory is enough to run GPT-OSS 20B at Q5_K_S quantization (15.8GB required).
What's the best quantization to use?
Q5_K_S is the highest-precision quantization that fits in your 16.0GB. It uses about 15.8GB of memory and 17.3GB 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.