Baidu's Unlimited-OCR Parses 40-Page PDFs in One Pass With Flat Memory Use

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Baidu has released Unlimited-OCR, an open-source model capable of processing multi-page PDFs in a single forward pass without the memory growth that typically plagues OCR pipelines. The model replaces standard attention layers with a mechanism called Reference Sliding Window Attention (R-SWA), which maintains a fixed-size token cache instead of an ever-growing one, keeping memory and latency constant across long documents. With 3 billion total parameters but only 500 million activated via a Mixture-of-Experts architecture, the model runs closer to the cost of a 500M model at inference time. It scored 93.23 on OmniDocBench v1.5, roughly 6.2 points above the DeepSeek-OCR baseline it was trained from, suggesting the efficiency gains did not come at the cost of accuracy. The model is released under an MIT license and requires an NVIDIA GPU with at least 12 GB of VRAM for practical use.

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