TensorRT: NVIDIA GPU Specific Optimization

TensorRT is NVIDIA's inference compiler and runtime specifically designed to extract maximum performance from NVIDIA GPUs. Unlike general purpose compilers, TensorRT leverages deep knowledge of GPU microarchitecture, tensor cores, and memory hierarchies to apply aggressive hardware specific optimizations. The result is often the fastest inference you can achieve on NVIDIA hardware, but with the tradeoff of vendor lock in and limited operator coverage for niche operations. The optimization pipeline starts with graph level transformations. TensorRT fuses layers like convolution, bias add, and ReLU into single optimized kernels to minimize memory bandwidth and kernel launch overhead. It then selects kernels from a library of thousands of precompiled variants, choosing based on layer parameters, batch size, and precision. For operations where no prebuilt kernel exists, TensorRT can generate custom CUDA code. Precision calibration is a standout feature: TensorRT can automatically convert FP32 models to FP16 or INT8 with minimal accuracy loss, using calibration data to determine per layer quantization ranges. Concrete performance gains are striking. On an RTX A4000, switching from ONNX Runtime FP16 to TensorRT FP16 often yields 3x higher throughput, and 12x compared to framework FP32 baselines for convolutional neural networks. For ResNet class models on cloud T4 GPUs, a compiled TensorRT engine in FP16 can serve 300 to 600 images per second at batch 1 to 8. On edge devices like Jetson, TensorRT enables single digit millisecond latency for real time object detection at 30 FPS within tight power budgets. For transformers and large language models, fused multi head attention kernels and INT8 quantization can deliver 1.5 to 3x tokens per second improvements on A100 GPUs. The runtime component is equally important. TensorRT engines are prebuilt binary artifacts that encapsulate the entire execution plan, including memory allocation strategies and kernel scheduling. At serving time, the runtime loads the engine, preallocates workspace memory, and executes with deterministic latency. This eliminates JIT compilation overhead and makes latency predictable under load, which is critical for production services with strict Service Level Objectives (SLOs).

NVIDIA Triton Inference Server uses TensorRT as the primary backend for GPU models, automatically building and caching engines for incoming ONNX models

Meta production vision pipelines compile object detection models to TensorRT INT8 engines, achieving sub 10ms p99 latency on T4 instances for content moderation

Google Cloud AI Platform supports TensorRT optimization for user uploaded models, transparently accelerating inference on NVIDIA GPU nodes