TensorRT tuner searches model-specific build routes

NVIDIA explains how TensorRT’s Global Performance Tuner can explore different compiler build routes for each neural network, then choose the fastest engine that satisfies an accuracy limit. The approach addresses a weakness of shared heuristics: a setting that helps one model can slow another or exceed an application’s accuracy tolerance. The feature shipped in TensorRT 11.1.

NVIDIA Technical Blog · Oct 23, 2026

Why it matters

Inference teams can frame optimization as a measured search over model-specific choices, with accuracy enforced as a constraint.

More context

TensorRT builds engines through interdependent choices such as graph transformations, kernel selection, memory placement and quantization. The tuner lets users customize these routes and checks end-to-end performance and output accuracy for candidate engines.

Keep in mind

This article describes NVIDIA’s own product and theoretical framing; it does not provide an independent performance comparison. The post is part one of a series, with implementation details and best practices deferred to part two.

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