Can I Run Qwen3 VL 30B A3B Thinking on a NVIDIA RTX PRO 6000 Blackwell?
Runs at full precision (fp16). Zero quality loss.
12 quantizations fit your 96.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 63.2 GB | 64.7 GB | 1.1 GB | +32.8 GB |
| Q8_0 | 34.0 GB | 35.5 GB | 32.5 GB | +62.0 GB |
| Q6_K | 26.6 GB | 28.1 GB | 25.1 GB | +69.4 GB |
| Q5_K_M | 23.1 GB | 24.6 GB | 21.7 GB | +72.9 GB |
| Q5_K_S | 22.5 GB | 24.0 GB | 21.1 GB | +73.5 GB |
| Q4_1 | 20.4 GB | 21.9 GB | 19.2 GB | +75.6 GB |
| Q4_K_M | 19.9 GB | 21.4 GB | 18.6 GB | +76.2 GB |
| Q4_K_S | 18.8 GB | 20.3 GB | 17.5 GB | +77.2 GB |
| Q4_0 | 18.5 GB | 20.0 GB | 17.4 GB | +77.5 GB |
| Q3_K_M | 14.0 GB | 15.5 GB | 14.7 GB | +82.0 GB |
| Q3_K_S | 13.0 GB | 14.5 GB | 13.3 GB | +83.0 GB |
| Q2_K | 11.2 GB | 12.7 GB | 11.3 GB | +84.8 GB |
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Top-ranked open-source models that fit in 96.0GB.
FAQ
Can the NVIDIA RTX PRO 6000 Blackwell run Qwen3 VL 30B A3B Thinking?
Yes. The NVIDIA RTX PRO 6000 Blackwell's 96.0GB of VRAM is enough to run Qwen3 VL 30B A3B Thinking at fp16 quantization (63.2GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 96.0GB. It uses about 63.2GB of memory and 64.7GB 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.