configure onnx runtime to use the tensorrt and cuda execution providers with fallback
domain: onnxruntime.ai/docs/execution-providers · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Append execution providers to SessionOptions in priority order, e.g. OrtSessionOptionsAppendExecutionProvider_Tensorrt then _CUDA, so ONNX Runtime falls back if TensorRT can't run a given op
Set TensorRT-specific options such as workspace size, and enable ORT_TENSORRT_FP16_ENABLE or ORT_TENSORRT_INT8_ENABLE if your model and hardware support reduced precision
Pass per-session execution provider options rather than relying only on environment variables, since explicit session options override environment-variable settings
Create the InferenceSession with the ordered provider list and confirm via session introspection which provider actually executed each node
Benchmark against CUDA-only and CPU-only execution to quantify TensorRT engine-build overhead on first run versus steady-state latency
Known gotchas
TensorRT engine building happens lazily and can add significant first-inference latency — plan for engine cache/warm-up in latency-sensitive services
Provider order matters: ONNX Runtime falls back to the next provider in the list for unsupported ops, so the ordering changes which ops actually run on TensorRT
INT8 mode requires a calibration step; flipping ORT_TENSORRT_INT8_ENABLE without calibration data will not produce a valid accelerated engine
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