DeepSeek-OCR-2 Windows 11 Full Speed NPU Mode

The shortest path to running this model is by activating Hyper-V features.

Use the instructions provided below to complete the setup.

The installer automatically pulls the model (could be multiple GBs).

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

???? HASH-SUM: 99fd45420f5c9046c6ae0d020b9beaa5 | ???? Updated on: 2026-07-02 Verify Processor: high single-core performance needed for token latency RAM: minimum 16 GB for stable 8B model loading Disk Space: at least 100 GB for multiple local LLM variants GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

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.

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