How to Scale LLM Inference for AI Agents Using vLLM
SMRTR summary
Scaling LLM inference for AI agents gets tricky fast — one user request can trigger 20+ model calls, creating GPU memory pressure and scheduling bottlenecks. This tutorial walks through setting up a local vLLM server, connecting it to an AI agent via its OpenAI-compatible API, and explains how continuous batching, PagedAttention, and prefix caching tackle concurrent agent workloads.
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