Can I Run / GPT-OSS 120B / on NVIDIA H200
Can I Run GPT-OSS 120B on a NVIDIA H200?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
120B
GPU memory
141GB
Smallest quant
Q4_K_M
Best fit
Q8_0
4 quantizations fit your 141GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 128.5 GB | 130.0 GB | 127.5 GB | +12.5 GB |
| Q6_K | 99.8 GB | 101.3 GB | 98.8 GB | +41.2 GB |
| Q5_K_M | 86.2 GB | 87.7 GB | 85.2 GB | +54.8 GB |
| Q4_K_M | 73.8 GB | 75.3 GB | 72.8 GB | +67.3 GB |
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Full model details
GPT-OSS 120B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA H200 →
Top-ranked open-source models that fit in 141GB.
FAQ
Can the NVIDIA H200 run GPT-OSS 120B?
Yes. The NVIDIA H200's 141GB of VRAM is enough to run GPT-OSS 120B at Q8_0 quantization (128.5GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 141GB. It uses about 128.5GB of memory and 130.0GB 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.