Installation Guide Overview#
This guide provides complete instructions for installing and deploying TensorRT-RTX on supported platforms. Whether you are setting up TensorRT-RTX for the first time or upgrading an existing installation, this guide walks you through the process.
About TensorRT-RTX#
NVIDIA TensorRT for RTX (TensorRT-RTX) is an inference library that brings TensorRT optimization to NVIDIA RTX GPUs across desktops, laptops, and workstations. It uses a portable ahead-of-time (AOT) engine build plus just-in-time (JIT) compilation on the end-user GPU, so you do not need a separate full engine build for every target device.
For the two-phase compilation pipeline and ecosystem context, refer to the Architecture Overview. For runtime object lifetimes, threading, and memory contracts when embedding TensorRT-RTX, refer to How TensorRT-RTX Works.
Recommended First-Time Path#
Follow these steps in order. Each step links to the right topic; the sidebar section name only tells you where that page lives in the docs, not whether you must stay in one part of the guide.
1. Install (Installing TensorRT-RTX)
Check prerequisites: Verify supported hardware, OS, and dependencies → Prerequisites
Install the SDK: Download and set up TensorRT-RTX on your platform → Installing TensorRT-RTX
2. Verify and plan deployment (Getting Started)
Verify your install: Run the Quick Start Guide (download a sample ONNX model, build an engine, run inference on your RTX GPU)
Plan deployment: After Quick Start succeeds, use Build Your First Engine for bundling engines, portability, and runtime-cache storage
3. Your models and application (sidebar: Installing TensorRT-RTX and Inference Library)
Export or integrate models: ONNX Conversion Guide or Using TensorRT-RTX via PyTorch
Integrate in your app: Using the Native Runtime API