Can I Run / GPT-OSS 20B / on NVIDIA A100 40GB
Can I Run GPT-OSS 20B on a NVIDIA A100 40GB?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
21.5B
GPU memory
40.0GB
Smallest quant
Q2_K
Best fit
Q8_0
11 quantizations fit your 40.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 23.8 GB | 25.3 GB | 12.1 GB | +16.2 GB |
| Q6_K | 18.7 GB | 20.2 GB | 12.0 GB | +21.3 GB |
| Q5_K_M | 16.3 GB | 17.8 GB | 11.7 GB | +23.7 GB |
| Q5_K_S | 15.8 GB | 17.3 GB | 11.7 GB | +24.2 GB |
| Q4_1 | 14.4 GB | 15.9 GB | 11.6 GB | +25.6 GB |
| Q4_K_M | 14.0 GB | 15.5 GB | 11.6 GB | +26.0 GB |
| Q4_K_S | 13.3 GB | 14.8 GB | 11.6 GB | +26.7 GB |
| Q4_0 | 13.1 GB | 14.6 GB | 11.5 GB | +26.9 GB |
| Q3_K_M | 10.0 GB | 11.5 GB | 11.5 GB | +30.0 GB |
| Q3_K_S | 9.3 GB | 10.8 GB | 11.5 GB | +30.7 GB |
| Q2_K | 8.1 GB | 9.6 GB | 11.5 GB | +31.9 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 A100 40GB →
Top-ranked open-source models that fit in 40.0GB.
FAQ
Can the NVIDIA A100 40GB run GPT-OSS 20B?
Yes. The NVIDIA A100 40GB's 40.0GB of VRAM 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 40.0GB. 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.