Run dots.mocr Local Guide

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

Use the instructions provided below to complete the setup.

The framework seamlessly downloads the massive neural network binaries.

The deployment tool scans your environment and chooses the ideal parameters.

📦 Hash-sum → cce5e369681618b03d4c050f40c92a8d | 📌 Updated on 2026-06-27
  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Disk: 150+ GB for high-context vector database storage
  • Graphics: 12 GB VRAM minimum required for basic quantization

The dots.mocr model is a state‑of‑the‑art multimodal OCR system designed for high‑speed document processing. It combines vision and language modules to extract text from scanned images, handwritten notes, and natural‑scene photos with unprecedented accuracy. With a parameter count of 1.5 B, the model runs efficiently on consumer GPUs while maintaining real‑time inference speeds. The architecture incorporates a novel attention‑based layout analyzer that preserves structural relationships, enabling downstream tasks such as data entry and content summarization. dots.mocr also supports multilingual scripts, achieving over 90 % word‑error‑rate reduction on benchmark datasets compared to legacy solutions. Its modular design allows developers to fine‑tune specific components, making it a versatile choice for enterprise workflow automation.

Spec Value
Parameters 1.5 B
Input Types PDF, JPG, PNG, Handwritten
Supported Languages 100
Inference Speed >30 fps on RTX 3080
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