Deploying the GLM-4.5V locally via Hugging Face Transformers requires meeting a higher hardware configuration:
- GPU Requirements: High-performance NVIDIA GPUs with large video memory, such as the A100 or H100 series, are required to handle the computational demands of 106 billion parametric models
- software dependency: Python libraries such as transformers, torch, accelerate and Pillow need to be installed (
pip install transformers torch accelerate Pillow) - Deployment process: After downloading the model from Hugging Face Hub, load the model using AutoProcessor and AutoModelForCausalLM, taking care to set the
trust_remote_code=Truenamedtorch.bfloat16Data types to optimize graphics memory usage
Local deployment is suitable for scenarios that require model fine-tuning or offline use, but requires a higher technical threshold and maintenance costs than API calls.
This answer comes from the articleGLM-4.5V: A multimodal dialog model capable of understanding images and videos and generating codeThe
May not be reproduced without permission:AI productivity tools " What are the hardware requirements for local deployment of GLM-4.5V?
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