Quick Run GLM-OCR PC with NPU No Python Required

If you need a near-instant local setup, just fetch files via a basic curl request.

Kindly follow the on-screen instructions below.

1-click setup: the app automatically fetches the large weight files.

The program scans your VRAM and RAM to seamlessly apply optimal configurations.

🧾 Hash-sum — 0c19267b22dc1676b211ef38bfe94926 • 🗓 Updated on: 2026-06-26 Verify CPU: multi-threading optimized for fast prompt processing RAM: minimum 16 GB for stable 8B model loading Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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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