TensorRT

TensorRT parses a trained neural network graph and applies a series of graph optimization techniques to eliminate redundant operations. Layer fusion (or operator fusion) is a critical optimization that combines multiple consecutive layers—such as convolution, bias, and ReLU—into a single compute kernel. This reduces the number of GPU kernel launches and minimizes memory bandwidth bottlenecks by keeping intermediate data in on-chip caches. The result is a streamlined execution graph with significantly lower inference latency.

TensorRT supports mixed-precision inference, allowing models to run at FP32, FP16, and INT8 precision. The INT8 mode requires a calibration step that uses a representative dataset to determine optimal scaling factors for activations and weights, minimizing information loss during quantization. This process is distinct from generic post-training quantization; TensorRT's calibration algorithm selects per-tensor or per-channel dynamic ranges to preserve accuracy while delivering up to 2x throughput improvements compared to FP16 on hardware with Tensor Cores.

For each supported operation, TensorRT maintains a library of multiple CUDA kernel implementations called tactics. During the build phase, the optimizer performs auto-tuning by benchmarking candidate kernels on the target GPU hardware. It selects the tactic that minimizes execution time for the specific layer parameters, input dimensions, and tensor layouts. This hardware-aware selection ensures optimal performance on the deployed GPU architecture, whether it is an NVIDIA A100, L40S, or Jetson Orin.

TensorRT supports dynamic shapes for models that process variable input dimensions, such as natural language processing with varying sequence lengths. Users define an optimization profile specifying minimum, optimum, and maximum input shapes. The builder generates shape-agnostic kernels and shape-specific optimized kernels. At runtime, TensorRT selects the most efficient kernel for the actual input dimensions, avoiding the overhead of recompilation while maintaining performance across a range of workloads.

TensorRT's runtime leverages CUDA streams to enable concurrent execution of multiple inference requests. By overlapping kernel execution, memory transfers, and host-device communication across separate streams, the runtime maximizes GPU utilization. This is critical for high-throughput serving scenarios where batching alone cannot saturate the GPU. The runtime also manages internal thread pools to parallelize operations like plugin execution and data formatting, ensuring deterministic low-latency behavior under load.

For operations not natively supported by TensorRT, the SDK provides a plugin API that allows developers to implement custom CUDA kernels and integrate them into the optimized graph. Plugins define their own serialization, enqueue, and workspace management methods. This extensibility is essential for deploying models with novel operators—such as custom attention mechanisms or signal processing blocks—without sacrificing the performance benefits of the TensorRT runtime. Plugins are compiled into shared libraries and loaded dynamically.