Launch DeepSeek-OCR-2 Locally (No Cloud) Local Guide

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

Kindly follow the on-screen instructions below.

The installer auto-downloads and deploys the entire model pack.

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

📤 Release Hash: 4ef82304280f63216f5969436a7ccf2f • 📅 Date: 2026-07-01



  • CPU: 8-core / 16-thread recommended for orchestration
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: free: 80 GB on system drive for scratch space
  • Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

The DeepSeek-OCR-2 model sets a new benchmark in document understanding by combining high‑resolution image processing with a novel attention mechanism that captures contextual relationships across lines and paragraphs. Its architecture leverages a multi‑scale convolutional backbone, enabling robust performance on both printed and handwritten scripts while maintaining fast inference speeds on standard GPUs. A dedicated language‑agnostic tokenizer expands the model’s vocabulary to over 200 k subword units, supporting more than 100 languages and specialized domain terminologies. In comparative benchmarks, DeepSeek-OCR-2 achieves an average accuracy of 98.7 % on the DocVQA dataset, surpassing the previous state‑of‑the‑art by a margin of 1.4 %. The accompanying open‑source toolkit provides pre‑trained checkpoints, data augmentation pipelines, and a simple API, allowing developers to fine‑tune the model for custom OCR pipelines with minimal overhead.

Model name DeepSeek-OCR-2
Parameters 1.2B
Input resolution 1024×1024
Supported languages 100
Accuracy (DocVQA) 98.7%
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