olmOCR-2-7B-1025-FP8 5-Minute Setup
The shortest path to running this model is by activating Hyper-V features.
Refer to the action plan below to initialize the model.
The setup auto-downloads all needed files (several GBs).
You don’t need to tweak anything; the installer picks the highest performing setup.
📦 Hash-sum → b9758847772016858b1b6d6f89ba1a12 | 📌 Updated on 2026-07-01 - Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
- RAM: minimum 16 GB for stable 8B model loading
- Disk Space: at least 100 GB for multiple local LLM variants
- GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference
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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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