Can I Run Qwen3 VL 235B A22B Instruct on a NVIDIA B100?
Runs at Q5_K_M — good quality with reasonable headroom.
8 quantizations fit your 192GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 168.3 GB | 169.8 GB | 166.8 GB | +23.7 GB |
| Q5_K_S | 163.6 GB | 165.1 GB | 161.9 GB | +28.4 GB |
| Q4_K_M | 143.9 GB | 145.4 GB | 142.2 GB | +48.1 GB |
| Q4_K_S | 135.9 GB | 137.4 GB | 133.7 GB | +56.1 GB |
| Q3_K_L | 105.9 GB | 107.4 GB | 121.8 GB | +86.1 GB |
| Q3_K_M | 99.7 GB | 101.2 GB | 112.5 GB | +92.3 GB |
| Q3_K_S | 91.7 GB | 93.2 GB | 101.4 GB | +100.3 GB |
| Q2_K | 78.5 GB | 80.0 GB | 85.7 GB | +113.5 GB |
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Top-ranked open-source models that fit in 192GB.
FAQ
Can the NVIDIA B100 run Qwen3 VL 235B A22B Instruct?
Yes. The NVIDIA B100's 192GB of VRAM is enough to run Qwen3 VL 235B A22B Instruct at Q5_K_M quantization (168.3GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 192GB. It uses about 168.3GB of memory and 169.8GB 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.