OpenDCAI

OpenDCAI / Flash-MinerU

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Ray-based accelerator for MinerU VLM inference pipeline. Lightweight, multi-GPU friendly PDF → Markdown processing. 基于 Ray 的 MinerU VLM 推理加速器，轻量、低侵入，面向多 GPU / 国产算力环境的 PDF → Markdown 处理方案。

distributed-computing document-ai llm-inference mineru multi-gpu
31
3
100% credibility
Found Feb 07, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Flash-MinerU accelerates PDF-to-Markdown conversion by parallelizing the AI thinking step in the open-source MinerU project.

How It Works

1
🔍 Discover Flash-MinerU

You hear about a speedy tool that turns complex PDFs into clean, editable Markdown files.

2
📦 Install easily

With one simple command, you add it to your computer, no hassle.

3
📥 Grab the AI brain

Download the smart model that understands PDF layouts, ready in moments.

4
📁 Gather your PDFs

Collect the PDF files you want to convert, like research papers or reports.

5
🚀 Launch your converter

Create a simple setup with your files and model, then hit go – it processes batches lightning-fast using all your computer's power.

6
⏳ Watch it work

Sit back as it slices through pages, thinking smarter and faster than before.

✅ Enjoy perfect Markdown

Open folders full of beautifully structured Markdown, ready to edit or share.

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AI-Generated Review

What is Flash-MinerU?

Flash-MinerU is a Ray-based accelerator that speeds up MinerU's VLM inference pipeline for PDF to Markdown processing. It parallelizes the slowest stage—VLM calls—across multiple GPUs, turning batches of PDFs into structured Markdown outputs. Built in Python, it installs via pip and exposes a simple API like `engine.run(pdfs)` for lightweight, drop-in acceleration.

Why is it gaining traction?

It delivers ~4x end-to-end speedup on 8 A100 GPUs versus vanilla MinerU, even halving times on single GPUs, while keeping outputs identical. Multi-GPU friendly with low intrusion, it scales to clusters via Ray without rewriting MinerU logic. Devs love the pip simplicity and vLLM backend for high-throughput inference.

Who should use this?

Data scientists or ML engineers batch-processing academic papers, reports, or scanned docs into Markdown for RAG pipelines or knowledge bases. Ideal for teams with multi-GPU setups (NVIDIA or domestic chips) handling 100s of PDFs daily, but skipping if you're on CPU-only or small-scale.

Verdict

Promising accelerator for MinerU users chasing GPU inference speedups—test the quickstart on your PDFs. With 23 stars and 1.0% credibility, it's alpha-stage; solid benchmarks but light docs and no tests mean prototype use only until maturity grows.

(187 words)

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