Can I Run / GPT-OSS 20B / on Apple M3 Max (36GB)
Can I Run GPT-OSS 20B on a Apple M3 Max (36GB)?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
20.0B
GPU memory
36.0GB
Smallest quant
Q4_K_M
Best fit
Q8_0
4 quantizations fit your 36.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 22.3 GB | 23.8 GB | 21.3 GB | +13.8 GB |
| Q6_K | 17.5 GB | 19.0 GB | 16.5 GB | +18.5 GB |
| Q5_K_M | 15.2 GB | 16.7 GB | 14.2 GB | +20.8 GB |
| Q4_K_M | 13.1 GB | 14.6 GB | 12.1 GB | +22.9 GB |
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Full model details
GPT-OSS 20B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
Apple M3 Max (36GB) →
Top-ranked open-source models that fit in 36.0GB.
FAQ
Can the Apple M3 Max (36GB) run GPT-OSS 20B?
Yes. The Apple M3 Max (36GB)'s 36.0GB of unified memory is enough to run GPT-OSS 20B at Q8_0 quantization (22.3GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 36.0GB. It uses about 22.3GB of memory and 23.8GB 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.