The vLLM deployment delivers significant performance gains for dots.ocr:
- Reasoning Acceleration: vLLM's PagedAttention technology optimizes video memory usage to enable high throughput processing of 1.7B parametric models on a single card GPU.
- Servitization Support: By
vllm servecommand to start the API service for easy integration into the enterprise document processing pipeline. - Resource utilization optimization: Parameters
--gpu-memory-utilization 0.95can maximize the use of GPU resources, while the--tensor-parallel-sizeSupports multi-card expansion.
Compared with the native HuggingFace reasoning, the vLLM version can be 2-3 times faster in processing batch documents, which is especially suitable for scenarios that require real-time parsing. When deploying, you need to pay attention to the step of registering a custom model to vLLM (by modifying themodeling_dots_ocr_vllm).
This answer comes from the articledots.ocr: a unified visual-linguistic model for multilingual document layout parsingThe
May not be reproduced without permission:AI productivity tools " Why is it recommended to use vLLM to deploy dots.ocr?
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