DeepSeek-OCR is designed to be highly efficient, but its performance depends on the hardware configuration, document complexity, and compression mode you choose. At a minimum, it requires a modern GPU with at least 16 GB of VRAM to run comfortably, though 24 GB or more is recommended for large or complex documents. The system has been benchmarked primarily on NVIDIA A100 and H100 GPUs, where it demonstrates impressive throughput—processing up to 200,000 pages per day on a single A100 under standard 10× compression settings. The model also supports mixed-precision (FP16) computation, which allows faster inference without sacrificing accuracy. For smaller workloads, it can run on consumer-grade GPUs like the RTX 4090 or even on CPU-only setups, though performance will be significantly slower. Developers who want to scale across large datasets or continuous ingestion pipelines can take advantage of multi-GPU or distributed deployments using frameworks like DeepSpeed, Ray, or PyTorch Distributed.
The model’s throughput depends largely on the compression ratio and document complexity. Lower compression levels (e.g., 5×) preserve fine-grained details like handwriting or diagrams but increase token count and inference time. Higher compression (10×–20×) dramatically reduces token usage and speeds up processing but can slightly lower text fidelity. In practice, most production workflows balance between 8× and 12× compression for optimal performance and quality. Document characteristics also affect speed—simple text pages process quickly, while documents with tables, images, or multi-column layouts require additional computation. For example, a typical academic paper with diagrams might take around 0.4 seconds per page on an A100 GPU, whereas a simple invoice could be processed in 0.1 seconds per page. Developers can batch multiple pages together to maximize GPU utilization and achieve near-linear scaling with additional hardware.
For enterprise deployments or large-scale digitization projects, DeepSeek-OCR’s architecture makes it easy to scale horizontally. A 20-node GPU cluster, for instance, can process millions of pages daily, supporting real-time or near–real-time document ingestion for applications like retrieval-augmented generation (RAG) or compliance auditing. Because the model is fully open source and self-hosted, it can also be optimized for specific environments—running in containers, Kubernetes clusters, or even integrated into serverless GPU functions. Memory footprint can be reduced by using quantized weights or smaller model variants. In short, DeepSeek-OCR is flexible: it can run efficiently on a single workstation for smaller workloads or scale to high-throughput, multi-GPU systems capable of processing vast document collections at enterprise scale—all while maintaining consistent accuracy and speed.
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