Production document understanding systems saturate on GPU inference capacity, not worker count, and OCR latency—not LLM parsing—drives end-to-end throughput.
May 27, 2026
Summary
Teams building document extraction systems optimize for the wrong bottleneck. Scaling workers without isolating GPU-bound inference wastes resources; profiling reveals OCR is your actual latency wall, not the language model.
Why it matters
Teams building document extraction systems optimize for the wrong bottleneck. Scaling workers without isolating GPU-bound inference wastes resources; profiling reveals OCR is your actual latency wall, not the language model.
Implementation verdict
Applies to production document pipelines combining classification, OCR, and LLM extraction. Requires microservice separation of GPU inference from CPU orchestration, async IO handling, and horizontal scaling keyed to GPU capacity. Concrete patterns are ready now; implementation depends on your current stack.
Sources
Dev Signal
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