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Scaling Document AI: Microservice Patterns for Production Pipelines
π‘Learn why OCRβnot LLMsβis the real bottleneck in production document AI and how to architect around it.
β‘ 30-Second TL;DR
What Changed
Separates GPU-bound inference from CPU-bound orchestration to improve system efficiency.
Why It Matters
Provides practitioners with a blueprint to move beyond benchmarks and build production-grade document processing systems that handle thousands of pages per hour.
What To Do Next
Profile your document pipeline to verify if OCR is your primary latency bottleneck before scaling your LLM inference resources.
Who should care:Developers & AI Engineers
Key Points
- β’Separates GPU-bound inference from CPU-bound orchestration to improve system efficiency.
- β’Identified that OCR latency, rather than LLM parsing, is the primary bottleneck in document pipelines.
- β’System throughput is constrained by shared GPU capacity rather than the number of worker nodes.
- β’Utilizes asynchronous processing to handle IO-bound operations effectively at scale.
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Original source: ArXiv AI β