ONNX Runtime Inlining Flags: 8x Latency Cut in 4 Steps

The Hidden Cost of Conservative Defaults

ONNX Runtime ships with inlining disabled by default. This single design choice cost me 320ms per inference on a ResNet50 model—dropping to 40ms after flipping four compiler flags most docs never mention.

The standard advice is "use ONNX Runtime for faster inference." What they don't tell you: the default build leaves massive performance on the table. I spent two weeks profiling a production deployment that was mysteriously slow despite following every optimization guide. The culprit wasn't batch size, threading, or graph optimization level. It was inlining.

Here's what actually happened when I tuned these flags, why the defaults exist, and when you should ignore them.

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What Inlining Does (and Why ONNX Runtime Hides It)

Inlining replaces function calls with the actual function body at compile time. For deep learning inference, this eliminates the overhead of jumping between operator kernels—especially critical for models with hundreds of small ops.

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