How to Run olmOCR-2-7B-1025-FP8 on Copilot+ PC Quantized GGUF Local Guide

How to Run olmOCR-2-7B-1025-FP8 on Copilot+ PC Quantized GGUF Local Guide

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

Follow the step-by-step instructions below.

The setup auto-streams the model assets (expect a multi-GB download).

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

🛠 Hash code: e995207e37d33854f8478c1bfe981493 — Last modification: 2026-06-28



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: free: 80 GB on system drive for scratch space
  • 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.

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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