TensorRT Deployment

We export your model to ONNX format, then compile it with TensorRT. The conversion handles dynamic shapes (variable batch sizes, sequence lengths), custom operators via TensorRT plugins, and layer fusion optimizations. Models that can't fully convert run in hybrid mode — TensorRT-supported layers run optimized, unsupported layers fall back to the original framework. Every conversion gets validated for numerical accuracy.

FP16 conversion is straightforward — enable it and verify accuracy. INT8 quantization requires a calibration dataset to determine optimal scaling factors per layer. We run calibration with representative production data, then benchmark accuracy against FP32 baselines. Layers that lose too much precision in INT8 stay in FP16. The result: 2-4x speedup over FP32 with measured accuracy tradeoffs.

TensorRT engines are GPU-architecture specific — an engine built for A100 won't run on T4. We build engines for each GPU type in your fleet, tagged with model version and GPU target. Engine builds integrate into your CI/CD pipeline — a new model version triggers automatic engine compilation for all target architectures. Built engines get stored in your artifact registry alongside the source model.

TensorRT engines deploy via Triton Inference Server with dynamic batching enabled. We configure model warmup to avoid cold-start latency, instance groups to maximize GPU occupancy, and ensemble pipelines for models that need preprocessing or postprocessing steps. The serving endpoint is API-compatible with your existing integration — swapping in TensorRT requires no client changes.