How to Optimize NVIDIA Triton Inference Server for Throughput and Latency

This article provides a comprehensive tutorial on optimizing NVIDIA Triton Inference Server performance for both throughput and latency. It emphasizes that high GPU utilization is not the goal; instead, the process should start with defining a latency objective, establishing a repeatable baseline, and testing realistic concurrency. It covers dynamic batching for stateless models, sequence batching for stateful models, model instance placement, scheduler delay tuning, and concurrent execution. Tools like Perf Analyzer and Model Analyzer are used to measure and search configurations. The article also includes how to build a versioned model repository, promote configurations, and roll back changes, making it a practical guide for IT professionals deploying AI inference at scale.