Can I Run Qwen3 VL 32B Instruct on a NVIDIA RTX PRO 5000 Blackwell?
Runs at Q8_0 — near-lossless quality.
11 quantizations fit your 48.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 36.5 GB | 38.0 GB | 34.8 GB | +11.5 GB |
| Q6_K | 28.5 GB | 30.0 GB | 26.9 GB | +19.5 GB |
| Q5_K_M | 24.7 GB | 26.2 GB | 23.2 GB | +23.3 GB |
| Q5_K_S | 24.1 GB | 25.6 GB | 22.6 GB | +23.9 GB |
| Q4_1 | 21.9 GB | 23.4 GB | 20.6 GB | +26.1 GB |
| Q4_K_M | 21.3 GB | 22.8 GB | 19.8 GB | +26.8 GB |
| Q4_K_S | 20.1 GB | 21.6 GB | 18.8 GB | +27.9 GB |
| Q4_0 | 19.8 GB | 21.3 GB | 18.7 GB | +28.2 GB |
| Q3_K_M | 15.0 GB | 16.5 GB | 16.0 GB | +33.0 GB |
| Q3_K_S | 13.9 GB | 15.4 GB | 14.4 GB | +34.1 GB |
| Q2_K | 12.0 GB | 13.5 GB | 12.3 GB | +36.0 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 48.0GB.
FAQ
Can the NVIDIA RTX PRO 5000 Blackwell run Qwen3 VL 32B Instruct?
Yes. The NVIDIA RTX PRO 5000 Blackwell's 48.0GB of VRAM is enough to run Qwen3 VL 32B Instruct at Q8_0 quantization (36.5GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 48.0GB. It uses about 36.5GB of memory and 38.0GB 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.