olmOCR-2-7B-1025-FP8 on AMD/Nvidia GPU Direct EXE Setup

Docker offers the quickest path to setting up this model locally.

Follow the guidelines below to continue. The system automatically triggers a cloud download for all heavy weights.

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

🔒 Hash checksum: fec9b88d258a507a23d834bdb6810e81 • 📆 Last updated: 2026-06-28 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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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