Tensorrt Optimization

TensorRT optimization

TensorRT lowers a framework graph into a hardware-specific engine: it fuses layers, benchmarks kernels on your exact GPU, and runs reduced precision on the Tensor Cores. The gain is real, often 2x to 5x over eager PyTorch, but the engine is opaque and can quietly drop accuracy or reject an input shape you never tested. The method is to fix precision and shapes on purpose, then verify the engine still returns the right numbers.

Method

1. Export to ONNX and inspect the operators first. Convert at a fixed opset (17 or newer), then run onnx.checker and a shape-inference pass. An operator TensorRT cannot map falls back to a slow plugin or fails the build, and you want that surfaced now, not after a 20-minute engine build.
2. Get a baseline from trtexec before writing builder code. trtexec --onnx=model.onnx --saveEngine=model.plan produces a working engine and a latency number in one command. Treat it as the bar any custom Python builder has to match or beat before it earns its extra complexity.
3. Set precision against a stated accuracy budget. FP16 is the safe default and rarely moves accuracy. INT8 is smaller and faster but needs a calibration set of 100 to 1000 representative samples to fit per-tensor scales. FP8 on Hopper sits between them: measure the drop, do not assume it.
4. Give every dynamic axis an optimization profile. Specify min, opt, and max for each variable dimension, for example batch min=1, opt=8, max=32. TensorRT tunes kernels for the opt shape, so set opt to your common case, not the range midpoint. A runtime shape outside min-max is rejected outright.
5. Grant workspace so the tactic search can breathe. Pass a generous --memPoolSize=workspace:4096 so the builder can try more kernel tactics. Too little workspace silently narrows the search and leaves a faster fused kernel undiscovered, which costs latency you never see itemized.
6. Confirm fusion and precision in the engine inspector. Dump the engine layers and check that the fusions you expected fired and that INT8 layers did not revert to FP32. A fusion that did not happen is build time you spent for a speedup you did not receive.
7. Diff engine outputs against the framework on real inputs. Run the same batch through PyTorch and the engine, compare within a tolerance (atol near 1e-2 for FP16, looser for INT8), and check the task metric. Numeric closeness is necessary; a held-out accuracy number is the gate that actually matters.

Litmus tests

• Does the engine meet the latency floor on the target GPU, not a proxy card?
• Do the profile min and max shapes cover every batch size production sends?
• For INT8, was calibration data representative and accuracy measured, not hoped?
• Did the inspector confirm the intended fusions and precision truly applied?

Boundaries

This builds and validates one engine for one GPU family, and engines do not port across architectures, so rebuild per target. Serving the engine, fanning requests, and version routing belong to triton-inference-server. Continuous batching and KV-cache scheduling for LLMs live in inference-serving-optimization.