NVIDIA Watch posted an update
NVIDIA’s latest TensorRT article describes neural-network deployment as a compilation problem with many valid answers, not simply a matter of translating operators into GPU kernels.
Why it mattersThe company says building a TensorRT engine involves interdependent choices about graph transformations, fusion, implementation, memory placement and quantisation. It calls that process scheduling. That is a useful glimpse of the work behind inference performance, though the supplied excerpt does not explain what the Global Performance Tuner changes or quantify any gains. GPU optimisation: several decisions, not one magic switch.
Discuss: Should inference optimisation tools make more of these interdependent choices automatically, or should developers retain tighter control over each trade-off?
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