Can I Run Qwen3.5-27B on a NVIDIA RTX PRO 6000 Blackwell?
Runs at full precision (fp16). Zero quality loss.
11 quantizations fit your 96.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 56.6 GB | 58.1 GB | 0.9 GB | +39.4 GB |
| Q8_0 | 30.5 GB | 32.0 GB | 28.6 GB | +65.5 GB |
| Q6_K | 23.9 GB | 25.4 GB | 22.4 GB | +72.1 GB |
| Q5_K_M | 20.7 GB | 22.2 GB | 19.6 GB | +75.3 GB |
| Q5_K_S | 20.2 GB | 21.7 GB | 18.9 GB | +75.8 GB |
| Q4_1 | 18.4 GB | 19.9 GB | 17.2 GB | +77.6 GB |
| Q4_K_M | 17.9 GB | 19.4 GB | 16.7 GB | +78.2 GB |
| Q4_K_S | 16.9 GB | 18.4 GB | 15.8 GB | +79.1 GB |
| Q4_0 | 16.6 GB | 18.1 GB | 15.7 GB | +79.4 GB |
| Q3_K_M | 12.6 GB | 14.1 GB | 13.5 GB | +83.4 GB |
| Q3_K_S | 11.7 GB | 13.2 GB | 12.3 GB | +84.3 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 Qwen3.5-27B?
Yes. The NVIDIA RTX PRO 6000 Blackwell's 96.0GB of VRAM is enough to run Qwen3.5-27B at fp16 quantization (56.6GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 96.0GB. It uses about 56.6GB of memory and 58.1GB 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.