GLM-OCR For Low VRAM (6GB/8GB) For Beginners

The shortest path to running this model is by activating Hyper-V features.

Go through the configuration rules shown below.

The loader auto-caches the model archive (several GBs included).

You don't need to tweak anything; the installer picks the highest performing setup.

🗂 Hash: 692fc11d5ae6cd43291863cbf48e4b5d • Last Updated: 2026-06-27 Verify Processor: 6-core 3.5 GHz minimum required RAM: required: 16 GB absolute minimum for small models Storage:100 GB free space for HuggingFace cache folder Graphics: TensorRT-LLM / vLLM inference engine compatible chip

GLM-OCR is a lightweight vision-language model tailored specifically for advanced document understanding and structure preservation. The architecture integrates a 400M parameter CogViT visual encoder alongside a compact 500M parameter GLM language decoder to maximize layout analysis precision. Unlike classic character recognition engines, this framework introduces an innovative Multi-Token Prediction (MTP) loss mechanism to increase decoding throughput substantially while lowering system memory demands. It effortlessly reconstructs intricate multilingual tables, LaTeX formulas, and handwritten text into semantic Markdown or structured JSON outputs. The compact blueprint allows for highly accurate, state-of-the-art multi-page processing directly within resource-constrained edge computing environments.

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