Can I Run / GPT-OSS 20B / on AMD RX 7900 XT
Can I Run GPT-OSS 20B on a AMD RX 7900 XT?
Yes
Runs comfortably at Q6_K — minimal quality loss.
Model size
21.5B
GPU memory
20.0GB
Smallest quant
Q2_K
Best fit
Q6_K
10 quantizations fit your 20.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 18.7 GB | 20.2 GB | 12.0 GB | +1.3 GB |
| Q5_K_M | 16.3 GB | 17.8 GB | 11.7 GB | +3.7 GB |
| Q5_K_S | 15.8 GB | 17.3 GB | 11.7 GB | +4.2 GB |
| Q4_1 | 14.4 GB | 15.9 GB | 11.6 GB | +5.6 GB |
| Q4_K_M | 14.0 GB | 15.5 GB | 11.6 GB | +6.0 GB |
| Q4_K_S | 13.3 GB | 14.8 GB | 11.6 GB | +6.7 GB |
| Q4_0 | 13.1 GB | 14.6 GB | 11.5 GB | +6.9 GB |
| Q3_K_M | 10.0 GB | 11.5 GB | 11.5 GB | +10.0 GB |
| Q3_K_S | 9.3 GB | 10.8 GB | 11.5 GB | +10.7 GB |
| Q2_K | 8.1 GB | 9.6 GB | 11.5 GB | +11.9 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
AMD RX 7900 XT →
Top-ranked open-source models that fit in 20.0GB.
FAQ
Can the AMD RX 7900 XT run GPT-OSS 20B?
Yes. The AMD RX 7900 XT's 20.0GB of VRAM is enough to run GPT-OSS 20B at Q6_K quantization (18.7GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 20.0GB. It uses about 18.7GB of memory and 20.2GB 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.