Can I Run / GPT-OSS 20B / on Apple M5 (32GB)
Can I Run GPT-OSS 20B on a Apple M5 (32GB)?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
21.5B
GPU memory
32.0GB
Smallest quant
Q2_K
Best fit
Q8_0
11 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 23.8 GB | 25.3 GB | 12.1 GB | +8.2 GB |
| Q6_K | 18.7 GB | 20.2 GB | 12.0 GB | +13.3 GB |
| Q5_K_M | 16.3 GB | 17.8 GB | 11.7 GB | +15.7 GB |
| Q5_K_S | 15.8 GB | 17.3 GB | 11.7 GB | +16.2 GB |
| Q4_1 | 14.4 GB | 15.9 GB | 11.6 GB | +17.6 GB |
| Q4_K_M | 14.0 GB | 15.5 GB | 11.6 GB | +18.0 GB |
| Q4_K_S | 13.3 GB | 14.8 GB | 11.6 GB | +18.7 GB |
| Q4_0 | 13.1 GB | 14.6 GB | 11.5 GB | +18.9 GB |
| Q3_K_M | 10.0 GB | 11.5 GB | 11.5 GB | +22.0 GB |
| Q3_K_S | 9.3 GB | 10.8 GB | 11.5 GB | +22.7 GB |
| Q2_K | 8.1 GB | 9.6 GB | 11.5 GB | +23.9 GB |
Try it in the cloud first
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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 M5 (32GB) →
Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the Apple M5 (32GB) run GPT-OSS 20B?
Yes. The Apple M5 (32GB)'s 32.0GB of unified memory is enough to run GPT-OSS 20B at Q8_0 quantization (23.8GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 32.0GB. It uses about 23.8GB of memory and 25.3GB 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.