Can I Run NVIDIA Nemotron 3 Super 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 | 362.3 GB | 363.8 GB | 128.5 GB | +421.8 GB |
| Q6_K | 281.1 GB | 282.6 GB | 40.9 GB | +502.9 GB |
| Q5_K_M | 242.4 GB | 243.9 GB | 35.4 GB | +541.6 GB |
| Q5_K_S | 235.6 GB | 237.1 GB | 34.4 GB | +548.4 GB |
| Q4_1 | 213.5 GB | 215.0 GB | 31.4 GB | +570.5 GB |
| Q4_K_M | 207.1 GB | 208.6 GB | 30.2 GB | +576.9 GB |
| Q4_K_S | 195.7 GB | 197.2 GB | 28.6 GB | +588.4 GB |
| Q4_0 | 192.3 GB | 193.8 GB | 28.5 GB | +591.8 GB |
| Q3_K_L | 152.3 GB | 153.8 GB | 26.3 GB | +631.7 GB |
| Q3_K_M | 143.4 GB | 144.9 GB | 24.3 GB | +640.6 GB |
| Q3_K_S | 131.9 GB | 133.4 GB | 22.0 GB | +652.1 GB |
| Q2_K | 112.8 GB | 114.3 GB | 18.7 GB | +671.2 GB |
Try it in the cloud first
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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 NVIDIA Nemotron 3 Super?
Yes. The NVIDIA DGX Station (Blackwell Ultra)'s 784GB of unified memory is enough to run NVIDIA Nemotron 3 Super at Q8_0 quantization (362.3GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 784GB. It uses about 362.3GB of memory and 363.8GB 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.