Vision
How to deploy GLM-4.5V on a GPU cloud
A 108B vision-language model that reads images alongside text. Full specs, license and use cases.
GLM-4.5V size and hardware requirements
107.7B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
240.8 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 200.6 GB | 240.8 GB | RTX A6000 | 6 | $1.98/hr |
| FP8 (quantized) | 100.3 GB | 120.4 GB | RTX 4000 SFF Ada | 7 | $1.26/hr |
| INT4 (quantized) | 50.2 GB | 60.2 GB | A100 | 1 | $0.851/hr |
How to run GLM-4.5V
Run GLM-4.5V with vLLM
From zai-org/GLM-4.5V's own deployment docs.
vllm serve zai-org/GLM-4.5V \
--tensor-parallel-size 4 \
--tool-call-parser glm45 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
--served-model-name glm-4.5v \
--allowed-local-media-path / \
--media-io-kwargs '{"video": {"num_frames": -1}}'Source: https://huggingface.co/zai-org/GLM-4.5V/raw/main/README.md
Run GLM-4.5V with GGUF quantizations
Prebuilt GGUF weights published at mradermacher/GLM-4.5V-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf mradermacher/GLM-4.5V-GGUFDeploy GLM-4.5V on Aquanode
Aquanode has no one-click deploy template for GLM-4.5V; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (6× RTX A6000 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod