Can I Run GPT-OSS 120B on a NVIDIA RTX PRO 6000 Blackwell?
Runs at Q5_K_M — good quality with reasonable headroom.
2 quantizations fit your 96.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 86.2 GB | 87.7 GB | 85.2 GB | +9.8 GB |
| Q4_K_M | 73.8 GB | 75.3 GB | 72.8 GB | +22.3 GB |
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Top-ranked open-source models that fit in 96.0GB.
FAQ
Can the NVIDIA RTX PRO 6000 Blackwell run GPT-OSS 120B?
Yes. The NVIDIA RTX PRO 6000 Blackwell's 96.0GB of VRAM is enough to run GPT-OSS 120B at Q5_K_M quantization (86.2GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 96.0GB. It uses about 86.2GB of memory and 87.7GB 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.