Can I Run Qwen 3 235B A22B Instruct 2507 on a NVIDIA B100?
Runs at Q5_K_M — good quality with reasonable headroom.
2 quantizations fit your 192GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 167.8 GB | 169.3 GB | 166.8 GB | +24.2 GB |
| Q4_K_M | 143.5 GB | 145.0 GB | 142.5 GB | +48.5 GB |
Try it in the cloud first
Don't want to download Qwen 3 235B A22B Instruct 2507 just to try it? Use a hosted API or rent a GPU by the second.
Affiliate links — we earn a commission at no cost to you.
All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 192GB.
FAQ
Can the NVIDIA B100 run Qwen 3 235B A22B Instruct 2507?
Yes. The NVIDIA B100's 192GB of VRAM is enough to run Qwen 3 235B A22B Instruct 2507 at Q5_K_M quantization (167.8GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 192GB. It uses about 167.8GB of memory and 169.3GB 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.