Can I Run Qwen 3.5 9B on a NVIDIA RTX PRO 6000 Blackwell?
Runs at full precision (f32). Zero quality loss.
12 quantizations fit your 96.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| f32BEST | 39.8 GB | 41.3 GB | 1.8 GB | +56.2 GB |
| fp16 | 20.4 GB | 21.9 GB | 0.9 GB | +75.6 GB |
| Q8_0 | 11.3 GB | 12.8 GB | 9.5 GB | +84.7 GB |
| Q6_K | 9.0 GB | 10.5 GB | 7.5 GB | +87.0 GB |
| Q5_K_M | 7.9 GB | 9.4 GB | 6.6 GB | +88.1 GB |
| Q5_K_S | 7.7 GB | 9.2 GB | 6.4 GB | +88.3 GB |
| Q4_1 | 7.1 GB | 8.6 GB | 5.8 GB | +88.9 GB |
| Q4_K_M | 6.9 GB | 8.4 GB | 5.7 GB | +89.1 GB |
| Q4_K_S | 6.5 GB | 8.1 GB | 5.4 GB | +89.5 GB |
| Q4_0 | 6.5 GB | 8.0 GB | 5.4 GB | +89.5 GB |
| Q3_K_M | 5.1 GB | 6.6 GB | 4.7 GB | +90.9 GB |
| Q3_K_S | 4.7 GB | 6.2 GB | 4.3 GB | +91.3 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 96.0GB.
FAQ
Can the NVIDIA RTX PRO 6000 Blackwell run Qwen 3.5 9B?
Yes. The NVIDIA RTX PRO 6000 Blackwell's 96.0GB of VRAM is enough to run Qwen 3.5 9B at f32 quantization (39.8GB required).
What's the best quantization to use?
f32 is the highest-precision quantization that fits in your 96.0GB. It uses about 39.8GB of memory and 41.3GB 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.