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NVIDIA TensorRT for RTX

  • Documentation Home
NVIDIA TensorRT for RTX - Home NVIDIA TensorRT for RTX - Home

NVIDIA TensorRT for RTX

  • Documentation Home

Table of Contents

Getting Started

  • Release Notes
    • 1.6 (Latest)
    • 1.5
    • 1.4
    • 1.3
    • 1.2
    • 1.1
    • 1.0
  • Quick Start Guide
  • Build Your First Engine
  • Support Matrix

Installing TensorRT-RTX

  • Installation Guide Overview
  • Prerequisites
  • Installing TensorRT-RTX
  • ONNX Conversion Guide

Architecture

  • Architecture Overview
  • How TensorRT-RTX Works

Inference Library

  • Inference Library Overview
  • Using the Native Runtime API
  • C++ API Documentation
  • Python API Documentation
  • Using TensorRT-RTX via PyTorch
  • Advanced Topics
    • Engine Compatibility
    • Refitting Engines
    • Timing Cache Migration
    • Weight Streaming
  • Work With Quantized Types
  • Working with Dynamic Shapes
    • Dynamic Shapes: Core Concepts
    • Dynamic Shapes: Advanced Topics
  • Working with Runtime Cache
  • Working with RTX CUDA Graphs
  • Simultaneous Compute and Graphics
  • CPU-Only AOT and TensorRT-RTX Engines
  • Porting Guide for TensorRT Applications
  • TensorRT-RTX API Capture and Replay

Performance

  • Best Practices
  • Performance Benchmarking
  • Optimizing TensorRT-RTX Performance

API

  • C++ API
  • Python API
  • TensorRT-RTX Operators
  • ONNX GraphSurgeon API
  • Polygraphy API

Reference

  • Troubleshooting
  • Cybersecurity Disclosures
  • NVIDIA SOFTWARE LICENSE AGREEMENT
  • Advanced Topics
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Advanced Topics#

This section covers advanced TensorRT-RTX features and configuration options.

  • Engine Compatibility
    • Version Compatibility
    • Compatibility Checks
  • Refitting Engines
  • Timing Cache Migration
  • Weight Streaming
    • Configure Weight Streaming with the API
    • Configure Weight Streaming with tensorrt_rtx

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Using TensorRT-RTX via PyTorch

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Engine Compatibility

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Last updated on Jul 27, 2026.