Zero-Click Run olmOCR-2-7B-1025-FP8 via WebGPU (Browser)

The most rapid route to a local installation of this model is through Docker.

Follow the step-by-step instructions below.

1-click setup: the app automatically fetches the large weight files.

The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.

🔗 SHA sum: 18bc4144e428f0c670d093da00bbdd25 | Updated: 2026-06-25 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: minimum 16 GB for stable 8B model loading Disk: 150+ GB for high-context vector database storage GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

olmOCR-2-7B-1025-FP8 delivers state‑of‑the‑art optical character recognition with a massive 7‑billion parameter base, enabling unprecedented accuracy on complex document layouts. Built on the FP8 quantization scheme, it achieves a balanced trade‑off between inference speed and memory footprint, making it suitable for both cloud and edge deployments. The architecture incorporates a refined vision encoder that processes high‑resolution scans up to 1025 × 1025 pixels, preserving fine glyphs and contextual spacing. A dedicated language model head leverages multilingual tokenizers, supporting over 100 languages while maintaining a low error rate on cursive and printed text. Benchmark results show a 3.2 % absolute gain over the previous generation on the PubLayNet dataset, and the model is openly released under an permissive license for research and commercial use.

Model	olmOCR-2-7B-1025-FP8
Parameters	7 B
Input Resolution	1025 × 1025
Quantization	FP8
Supported Languages	100+
License	Permissive (Apache 2.0)

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