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Understanding TensorRT for RTX#

Content Type: Explanation — Understanding the architecture and concepts

NVIDIA TensorRT for RTX (TensorRT-RTX) is a specialization of NVIDIA TensorRT for the RTX product line. Like TensorRT, it contains a deep learning inference optimizer and a runtime for execution to enable high-performance inference. Unlike TensorRT, TensorRT-RTX performs Just-In-Time (JIT) compilation on the end-user machine. This approach greatly simplifies deployment when targeting a diverse set of end-user NVIDIA GPUs.

Key Difference from TensorRT: TensorRT-RTX uses JIT compilation on the end-user device rather than requiring ahead-of-time compilation on the target GPU. This enables a single engine to work across multiple RTX GPU models.

Who Should Use TensorRT-RTX

TensorRT-RTX is built for desktop-app developers who want to embed AI features such as:

  • Turning text into lifelike speech

  • Taking voice input from users

  • Generating images from prompts

The Desktop Challenge

Unlike server software, which usually targets a single known GPU, desktop applications must run on whatever graphics hardware end users have installed. TensorRT-RTX solves this by automatically optimizing AI inference for each GPU model without bloating installation times or inflating your package size.

Because many desktop programs (video games, creative suites, and so on) use the GPU simultaneously for rendering, TensorRT-RTX ensures that your AI workloads never steal performance from graphics. This approach keeps frame rates smooth and users engaged.

How It Works: Two-Phase Compilation

After you train your deep learning model in a framework of your choice, TensorRT-RTX enables you to run it with higher throughput and lower latency. Compilation proceeds in two phases:

TensorRT-RTX two-phase compilation: AOT optimizer creates portable engine, then JIT compilation optimizes for specific GPU

Phase 1: Ahead-of-Time (AOT) Optimization

The AOT optimizer translates the neural network into a TensorRT-RTX engine file (also known as a JIT-able engine). This step typically takes 20-30 seconds. This engine is portable across multiple RTX GPU models.

Phase 2: Just-In-Time (JIT) Compilation

Run at inference time, the system JIT-compiles this engine into an executable inference plan with an optimized inference strategy for the specific GPU. This includes the concrete choice of computation kernels. This step is very fast at the time of first inference invocation (under 5 seconds latency for most models). Runtime caching can optionally speed up subsequent invocations even more.

Why This Approach Works

This two-phase approach balances portability with performance:

  • AOT phase: Creates a single portable engine (fast, can run on CPU)

  • JIT phase: Optimizes for the specific GPU the user has (happens once on their machine)

  • Result: Simple deployment with near-optimal performance

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Next Steps

This section covers the basic installation, conversion, and runtime options available in TensorRT-RTX. Here is a summary of each topic:

Installing TensorRT-RTX - Installing TensorRT-RTX - We provide multiple, simple ways of installing TensorRT-RTX.

Example Deployment Using ONNX - This section examines the basic steps to convert and deploy your model. It introduces concepts used in the rest of the guide and walks you through the decisions you must make to optimize inference execution.

ONNX Conversion and Deployment - We provide a broad overview of ONNX exports from different training frameworks.

Using the TensorRT-RTX Runtime API - This section provides a tutorial on running a simple convolutional neural network using the TensorRT-RTX C++ and Python API.