Can I Run GPT-OSS 20B on a NVIDIA RTX A4000?
Runs at Q5_K_M — good quality with reasonable headroom.
2 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 15.2 GB | 16.7 GB | 14.2 GB | +0.8 GB |
| Q4_K_M | 13.1 GB | 14.6 GB | 12.1 GB | +2.9 GB |
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Top-ranked open-source models that fit in 16.0GB.
FAQ
Can the NVIDIA RTX A4000 run GPT-OSS 20B?
Yes. The NVIDIA RTX A4000's 16.0GB of VRAM is enough to run GPT-OSS 20B at Q5_K_M quantization (15.2GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 16.0GB. It uses about 15.2GB of memory and 16.7GB 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.