I had about 940,000 PDFs to process. Running VLMs over a million pages is slow and expensive, and that gap is only getting worse as OCR moves toward transformer and VLM-based approaches. They’re great for complex understanding, but throughput and cost can become a bottleneck at scale.
PaddleOCR (the non VL version), in my opinion the best non-VLM open source OCR, only handled ~15 img/s on my RTX 5090, which was still too slow. PaddleOCR-VL was crawling at 2 img/s with vLLM.
PaddleOCR runs single-threaded Python with FP32 inference and no kernel fusion. Turbo-OCR replaces that with C++/CUDA, FP16 TensorRT, fused kernels, batched recognition, and multi-stream pipeline pooling. It takes images and PDFs via HTTP/gRPC and returns bounding boxes, text, and layout regions (PP-DocLayoutV3, 25 classes).
Layout is toggleable per request and only adds ~20% to inference time.
Results: 270 img/s on text-heavy pages without layout, 1,200+ on sparse ones. Works well for real-time RAG where you need a document indexed instantly, or for bulk processing large collections cheaply.
Trade-offs: complex table extraction and structured output (invoice → JSON) still need VLM-based OCR like PaddleOCR-VL. I'm working on bringing structured extraction, markdown output, table parsing, and more languages to Turbo-OCR while sacrificing as little speed as possible..
Tested on Linux, RTX 50-series, CUDA 13.2.
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