olmOCR-2-7B-1025-FP8 Locally (No Cloud) Local Guide

The fastest method for installing this model locally is by using Docker.

Follow the guidelines below to continue.

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

To guarantee smooth performance, the installation process auto-selects the best possible options for your PC.

🧾 Hash-sum — 9133085fb7463d6b6b137983a80cfdc2 • 🗓 Updated on: 2026-06-24 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: required: 16 GB absolute minimum for small models Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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.

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