TensorRT

TensorRT is a high-performance deep learning inference optimizer and runtime library developed by NVIDIA. It optimizes trained neural network models for deployment on NVIDIA GPUs, reducing latency and increasing throughput through techniques like layer fusion, precision calibration, and kernel auto-tuning. It supports frameworks like TensorFlow, PyTorch, and ONNX, enabling efficient inference in production environments.

Also known as: NVIDIA TensorRT, Tensor RT, TRT, NVIDIA Inference Optimizer, TensorRT SDK

Why learn TensorRT?

Developers should use TensorRT when deploying deep learning models in real-time applications such as autonomous vehicles, video analytics, or recommendation systems, where low latency and high throughput are critical. It is essential for optimizing models on NVIDIA hardware to maximize GPU utilization and reduce inference costs in cloud or edge deployments.

Learning Resources

• docs: NVIDIA TensorRT Documentation
• tutorial: TensorRT Quick Start Guide
• course: Deep Learning Inference with TensorRT Course
• video: TensorRT Tutorial on YouTube