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This release advances document intelligence by introducing a three-mode pipeline that significantly improves throughput on B200 GPUs while maintaining high accuracy via Surya OCR. The architecture's ability to dynamically switch between GPU-accelerated VLM layouts and CPU-only text extraction offers practical efficiency gains for scaling RAG pipelines without sacrificing quality.
Datalab Marker v2 vs MinerU, Docling, and Liteparse: Benchmark Breakdown
Datalab rewrote Marker as a three-mode pipeline. Version 2 hits 76.0 on olmOCR-bench and sustains 2.9 pages per second on one B200 — over 5× MinerU's pipeline backend, while beating Docling on both accuracy and speed. Here's how it compares against MinerU, Docling and LiteParse, and which one fits your use case. The post Datalab Marker v2 vs MinerU, Docling, and Liteparse: Benchmark Breakdown appeared first on MarkTechPost.
Datalab has released Marker 2, a full rewrite of its open source document conversion pipeline. Marker converts PDF, image, PPTX, DOCX, XLSX, HTML, and EPUB files into markdown, JSON, HTML, or chunks. The Datalab team rebuilt it around three components shipped over the preceding months: Surya OCR 2, a 20M-param fast layout model, and a rebuilt pdftext that is 3× faster than the previous one. The main result comes from olmOCR-bench, a third-party benchmark from Allen AI. Marker 2’s balanced mode scores 76.0% overall and 83.5% on born-digital PDFs. It sustains 2.9 pages per second on a single B200 GPU. That is over 5× the throughput of MinerU’s pipeline backend, which scores 72.7% at 0.54 pages per second. Docling scores 50.3% at 2.1 pages per second on the same harness. Marker 2 is out now – up to…
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