The fastest method for installing this model locally is by using Docker.

Follow the guidelines below to continue.

Then, execute the docker-compose up command to launch the model.

📘 Build Hash: aad012bfbc001cd280385d9decaeb325 • 🗓 2026-06-22 Verify CPU: multi-threading optimized for fast prompt processing RAM: minimum 16 GB for stable 8B model loading Disk Space: free: 80 GB on system drive for scratch space 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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