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How to Autostart DeepSeek-OCR-2 with Native FP4

Running this model locally is fastest when deployed through a PowerShell script.

Kindly follow the on-screen instructions below.

The loader auto-caches the model archive (several GBs included).

The installer diagnoses your environment to deploy the most compatible profile.

🖹 HASH-SUM: 5d7426fb93ed09a0b13bc64c5b926c41 | 📅 Updated on: 2026-07-07



  • CPU: multi-threading optimized for fast prompt processing
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Disk Space: free: 80 GB on system drive for scratch space
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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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