@itsafiz: It really isn't an exaggeration! LiteParse clocks in at an average of 3ms per page for a reason: it skips the heavy AI …

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Summary

LiteParse is a fast document parsing tool that runs locally, achieving ~3ms per page by skipping heavy AI and cloud overhead. It uses deterministic layout heuristics and selective OCR to output structured Markdown, making it ideal for real-time RAG pipelines and coding agents.

It really isn't an exaggeration! LiteParse clocks in at an average of 3ms per page for a reason: it skips the heavy AI processing and cloud overhead entirely. Here is exactly how it pulls off that kind of speed: - Purely Local & Lightweight: It runs completely on your machine (built on a Rust core with a native PDFium C library) rather than sending files over the network to a distant cloud server. - No Heavy VLMs/GPUs: Instead of using an expensive, slow Vision-Language Model to "read" the page layout, it relies on fast, deterministic layout heuristics and projects text onto a spatial grid.   - Selective OCR: It only activates OCR (using lightweight engines like Tesseract) when it encounters scanned pages or embedded images; otherwise, it extracts native text layers directly.   Because it reconstructs headings, tables, and lists into structured Markdown almost instantly, it's a massive win for real-time RAG pipelines and coding agents that need a quick first pass over documents. Quick demo
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Cached at: 06/28/26, 03:56 AM

It really isn’t an exaggeration!

LiteParse clocks in at an average of 3ms per page for a reason: it skips the heavy AI processing and cloud overhead entirely.

Here is exactly how it pulls off that kind of speed:

  • Purely Local & Lightweight: It runs completely on your machine (built on a Rust core with a native PDFium C library) rather than sending files over the network to a distant cloud server.

  • No Heavy VLMs/GPUs: Instead of using an expensive, slow Vision-Language Model to “read” the page layout, it relies on fast, deterministic layout heuristics and projects text onto a spatial grid.  

  • Selective OCR: It only activates OCR (using lightweight engines like Tesseract) when it encounters scanned pages or embedded images; otherwise, it extracts native text layers directly.  

Because it reconstructs headings, tables, and lists into structured Markdown almost instantly, it’s a massive win for real-time RAG pipelines and coding agents that need a quick first pass over documents.

Quick demo

Jerry Liu (@jerryjliu0): LiteParse is unreasonably good for document parsing

✅ It is the fastest document parsing tool out there - average parse time per page is 3ms ⚡️⚡️ ✅ Now that we support markdown, it tops opendataloader-bench, OlmOCR-bench, and ParseBench in terms of accuracy ✅ It supports 50+

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