Can I Run GPT-OSS 20B on a AMD Ryzen AI Max 385 (32GB)?
Runs at Q8_0 — near-lossless quality.
11 quantizations fit your 24.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 23.8 GB | 25.3 GB | 12.1 GB | +0.2 GB |
| Q6_K | 18.7 GB | 20.2 GB | 12.0 GB | +5.3 GB |
| Q5_K_M | 16.3 GB | 17.8 GB | 11.7 GB | +7.7 GB |
| Q5_K_S | 15.8 GB | 17.3 GB | 11.7 GB | +8.2 GB |
| Q4_1 | 14.4 GB | 15.9 GB | 11.6 GB | +9.6 GB |
| Q4_K_M | 14.0 GB | 15.5 GB | 11.6 GB | +10.0 GB |
| Q4_K_S | 13.3 GB | 14.8 GB | 11.6 GB | +10.7 GB |
| Q4_0 | 13.1 GB | 14.6 GB | 11.5 GB | +10.9 GB |
| Q3_K_M | 10.0 GB | 11.5 GB | 11.5 GB | +14.0 GB |
| Q3_K_S | 9.3 GB | 10.8 GB | 11.5 GB | +14.7 GB |
| Q2_K | 8.1 GB | 9.6 GB | 11.5 GB | +15.9 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 24.0GB.
FAQ
Can the AMD Ryzen AI Max 385 (32GB) run GPT-OSS 20B?
Yes. The AMD Ryzen AI Max 385 (32GB)'s 24.0GB 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 24.0GB. 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.