Install GLM-OCR with 1M Context Easy Build

For an instant local deployment, running a pre-configured shell script is ideal.

Use the instructions provided below to complete the setup.

The setup auto-downloads all needed files (several GBs).

To save you time, the system will automatically determine efficient resource allocation.

🧾 Hash-sum — 63f1f63881d3d3bee8d1046334ef9535 • 🗓 Updated on: 2026-06-25 Verify Processor: 6-core 3.5 GHz minimum required RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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