onnxruntime-ep-nv-tensorrt-rtx

NVIDIA TensorRT RTX Execution Provider plugin for ONNX Runtime.

Enables hardware-accelerated inference on NVIDIA RTX GPUs (Ampere / RTX 30xx and later) via the ORT Plugin EP ABI.

About NVIDIA TensorRT for RTX

NVIDIA® TensorRT™ for RTX (TensorRT-RTX) is an inference optimization library dedicated for deploying AI inference on NVIDIA GeForce RTX GPUs. It is a great choice for developers building applications that must run on Windows or Linux PCs, laptops, or workstations.

This package bundles the TensorRT-RTX runtime libraries alongside the ONNX Runtime EP plugin so that no separate TensorRT-RTX installation is required.

For more information about TensorRT-RTX, visit https://developer.nvidia.com/tensorrt-rtx.
Online documentation: https://docs.nvidia.com/deeplearning/tensorrt-rtx/latest/index.html
License agreement: https://docs.nvidia.com/deeplearning/tensorrt-rtx/latest/reference/sla.html

References

Requirements

NVIDIA RTX GPU (Ampere or later)
NVIDIA GPU driver with CUDA 13 support
pip install onnxruntime>=1.24

Installation

pip install onnxruntime>=1.24
pip install onnxruntime-ep-nv-tensorrt-rtx

Usage

import onnxruntime as ort
import onnxruntime_ep_nv_tensorrt_rtx as trt_ep

# Register the EP plugin
ort.register_execution_provider_library(trt_ep.get_ep_name(), trt_ep.get_library_path())

# List available devices
devices = [d for d in ort.get_ep_devices() if d.ep_name == trt_ep.get_ep_name()]
print(f"TensorRT RTX devices: {len(devices)}")

# Create session with EP
so = ort.SessionOptions()
so.add_provider_for_devices(devices, {})
sess = ort.InferenceSession("model.onnx", sess_options=so)