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In this second part, we focus on the practical implementation of this high-performance pipeline. We show step-by-step how to set up a dedicated, fast processing server on your own NVIDIA GPU using Podman (on Rocky Linux) and the vLLM inference engine. We then build an asynchronous Python client to fully leverage the GPU's power and process even large stacks of documents.

In this first part we look at the conceptual advantages of Large Language Models (LLMs) in document analysis. The technical implementation and practical code examples of the two contrasting pipelines will follow in detail in an accompanying article.