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

If you want the fastest local installation for this model, use Docker.

Follow the guidelines below to continue.

The setup auto-streams the model assets (expect a multi-GB download).

The automated installation script takes care of everything by tailoring the setup perfectly to your system specs.

🔒 Hash checksum: 27fe4ef40935472c245d9ecc597fcd8f • 📆 Last updated: 2026-06-28 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 32 GB or higher for smooth 32k context lengths Disk Space: required: fast PCIe 4.0 drive for instant boots Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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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