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GLM OCR

GLM‑OCR is a 0.9B‑parameter multimodal OCR model that uses a CogViT vision encoder and GLM decoder with Multi‑Token Prediction to deliver state‑of‑the‑art document parsing accuracy while remaining small enough for fast, fully local deployment.
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How to Get Started

Step 1

Sign Up and get your Api Key and use with UNLIMITED tokens for 30 days.

Step 2

Paste the URL from Huggingface repository. (Ex: https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4)

Step 3

Choose the GPU machine to deploy.

That’s all! You’re ready to use the model in few minutes without infrastructure complexity in few minutes.

Document Parsing & Information Extraction

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Real-World Scenarios Performance

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Speed Test

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Applications & Use Cases

  • High‑accuracy OCR for PDFs, scanned documents, contracts, and receipts, including mixed text, stamps, seals, and complex layouts.
  • Table and form extraction that recovers structured data from financial statements, invoices, reports, and spreadsheets with SOTA OmniDocBench performance.
  • Formula and code recognition in scientific papers, textbooks, and developer documentation, turning screenshots and PDFs into editable LaTeX or source code.
  • Private, on‑device document parsing on laptops, workstations, or edge boxes thanks to the 0.9B size and <1.5 GB VRAM requirement (≈1 GB when quantized).
  • Pre‑processing stage in RAG pipelines, where GLM‑OCR converts large batches of PDFs and images into clean, layout‑aware text chunks for downstream LLMs.
  • Automation in back‑office workflows (KYC, compliance, backlogs) where robust OCR across stamps, signatures, and noisy scans is critical for reliability.