Can I Run / GPT-OSS 20B / on NVIDIA RTX A6000
Can I Run GPT-OSS 20B on a NVIDIA RTX A6000?
Yes
Runs at full precision (fp16). Zero quality loss.
Model size
20.0B
GPU memory
48.0GB
Smallest quant
Q4_K_M
Best fit
fp16
5 quantizations fit your 48.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 41.0 GB | 42.5 GB | 40.0 GB | +7.0 GB |
| Q8_0 | 22.3 GB | 23.8 GB | 21.3 GB | +25.8 GB |
| Q6_K | 17.5 GB | 19.0 GB | 16.5 GB | +30.5 GB |
| Q5_K_M | 15.2 GB | 16.7 GB | 14.2 GB | +32.8 GB |
| Q4_K_M | 13.1 GB | 14.6 GB | 12.1 GB | +34.9 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
NVIDIA RTX A6000 →
Top-ranked open-source models that fit in 48.0GB.
FAQ
Can the NVIDIA RTX A6000 run GPT-OSS 20B?
Yes. The NVIDIA RTX A6000's 48.0GB of VRAM is enough to run GPT-OSS 20B at fp16 quantization (41.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 48.0GB. It uses about 41.0GB of memory and 42.5GB 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.