GPT-OSS 20B — VRAM Requirements
Written by Jakub Rusinowski · Last updated August 15, 2026
How much GPU VRAM you need to run GPT-OSS GPT-OSS 20B by OpenAI locally, a 20B-parameter model. Figures are quantized weights + KV cache + framework overhead, computed from the model's parameter count and published architecture — not a throughput model. See /en/methodology.
GPT-OSS 20B needs about 13 GB VRAM at Q4_K_M.
VRAM by Quantization
| Quant | Bits/weight | Weights | Total VRAM |
|---|---|---|---|
| Q2_K | 2.63 | 6.6 GB | 7.8 GB |
| Q3_K_M | 3.41 | 8.5 GB | 9.7 GB |
| Q4_K_M | 4.83 | 12.1 GB | 13.3 GB |
| Q5_K_M | 5.67 | 14.2 GB | 15.4 GB |
| Q6_K | 6.56 | 16.4 GB | 17.6 GB |
| Q8_0 | 8.50 | 21.3 GB | 22.5 GB |
| F16 | 16.00 | 40.0 GB | 41.2 GB |
Switch quantization in the interactive calculator, or see the full GPT-OSS model page.
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Model creators: paste this into your Hugging Face model card README to link readers straight to this VRAM breakdown.
[](https://llmconfigurator.com/en/vram-calculator/gpt-oss-20b?utm_source=badge&utm_medium=referral&utm_campaign=readme_badge&utm_content=gpt-oss-20b)Estimates only — actual VRAM varies with context length, batch size, runtime and KV-cache settings.