Analysis
NVIDIA's October 23 technical explainer describes the Global Performance Tuner, a feature released in TensorRT 11.1. It broadens the build routes considered when compiling an inference engine and evaluates performance subject to accuracy requirements.
The authors explain why optimizing individual kernels can fail to improve a complete model. Fusion, memory traffic, quantization and scheduling interact, so a locally faster choice may produce a slower engine or violate the application's accuracy tolerance.
Our reading: define acceptable output error before comparing speed. Keep the target hardware and evaluation workload fixed, then measure the entire engine. The article explains an optimization approach; it does not provide a universal speed-up guarantee. Publication of this guide is distinct from the feature's original release.
Source: Scaling NVIDIA TensorRT Performance Optimization Beyond a Single Build Route with the Global Performance Tuner, Part 1 · 2026-10-23
Why it matters
Accept a faster build only after it passes the application's accuracy and end-to-end performance checks.