Can I Run GLM-5 on a NVIDIA DGX Station (Blackwell Ultra)?
Runs at full precision (fp16). Zero quality loss.
5 quantizations fit your 784GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 461.0 GB | 462.5 GB | 460.0 GB | +323.0 GB |
| Q8_0 | 245.4 GB | 246.9 GB | 244.4 GB | +538.6 GB |
| Q6_K | 190.5 GB | 192.0 GB | 189.5 GB | +593.5 GB |
| Q5_K_M | 164.3 GB | 165.8 GB | 163.3 GB | +619.7 GB |
| Q4_K_M | 140.4 GB | 141.9 GB | 139.4 GB | +643.6 GB |
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Top-ranked open-source models that fit in 784GB.
FAQ
Can the NVIDIA DGX Station (Blackwell Ultra) run GLM-5?
Yes. The NVIDIA DGX Station (Blackwell Ultra)'s 784GB of unified memory is enough to run GLM-5 at fp16 quantization (461.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 784GB. It uses about 461.0GB of memory and 462.5GB 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.