Can I Run Qwen 3 Coder on a NVIDIA DGX Station (Blackwell Ultra)?
Runs at Q8_0 — near-lossless quality.
12 quantizations fit your 784GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 511.0 GB | 512.5 GB | 32.5 GB | +273.0 GB |
| Q6_K | 396.4 GB | 397.9 GB | 25.1 GB | +387.6 GB |
| Q5_K_M | 341.8 GB | 343.3 GB | 21.7 GB | +442.2 GB |
| Q5_K_S | 332.2 GB | 333.7 GB | 21.1 GB | +451.8 GB |
| Q4_1 | 301.0 GB | 302.5 GB | 19.2 GB | +483.0 GB |
| Q4_K_M | 292.0 GB | 293.5 GB | 18.6 GB | +492.0 GB |
| Q4_K_S | 275.8 GB | 277.3 GB | 17.5 GB | +508.2 GB |
| Q4_0 | 271.0 GB | 272.5 GB | 17.4 GB | +513.0 GB |
| Q3_K_L | 214.6 GB | 216.1 GB | 14.6 GB | +569.4 GB |
| Q3_K_M | 202.0 GB | 203.5 GB | 14.7 GB | +582.0 GB |
| Q3_K_S | 185.8 GB | 187.3 GB | 13.3 GB | +598.2 GB |
| Q2_K | 158.8 GB | 160.3 GB | 11.3 GB | +625.2 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 784GB.
FAQ
Can the NVIDIA DGX Station (Blackwell Ultra) run Qwen 3 Coder?
Yes. The NVIDIA DGX Station (Blackwell Ultra)'s 784GB of unified memory is enough to run Qwen 3 Coder at Q8_0 quantization (511.0GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 784GB. It uses about 511.0GB of memory and 512.5GB 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.