Can I Run GLM-5 on a NVIDIA H200?
Yes
Runs at Q4_K_M — good quality with reasonable headroom.
Model size
230B
GPU memory
141GB
Smallest quant
Q4_K_M
Best fit
Q4_K_M
1 quant fit your 141GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_K_MBEST | 140.4 GB | 141.9 GB | 139.4 GB | +0.6 GB |
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Full model details
GLM-5 →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA H200 →
Top-ranked open-source models that fit in 141GB.
FAQ
Can the NVIDIA H200 run GLM-5?
Yes. The NVIDIA H200's 141GB of VRAM is enough to run GLM-5 at Q4_K_M quantization (140.4GB required).
What's the best quantization to use?
Q4_K_M is the highest-precision quantization that fits in your 141GB. It uses about 140.4GB of memory and 141.9GB 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.