<!-- # Copyright 2018-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of NVIDIA CORPORATION nor the names of its # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY # OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -->

[!WARNING] You are currently on the main branch which tracks under-development progress towards the next release. The current release is version 2.67.0 and corresponds to the 26.03 container release on NVIDIA GPU Cloud (NGC).

Triton Inference Server

Triton Inference Server is an open source inference serving software that streamlines AI inferencing. Triton enables teams to deploy any AI model from multiple deep learning and machine learning frameworks, including TensorRT, PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more. Triton Inference Server supports inference across cloud, data center, edge and embedded devices on NVIDIA GPUs, x86 and ARM CPU, or AWS Inferentia. Triton Inference Server delivers optimized performance for many query types, including real time, batched, ensembles and audio/video streaming. Triton inference Server is part of NVIDIA AI Enterprise, a software platform that accelerates the data science pipeline and streamlines the development and deployment of production AI.

Major features include:

• Supports multiple deep learning frameworks
• Supports multiple machine learning frameworks
• Concurrent model execution
• Dynamic batching
• Sequence batching and implicit state management for stateful models
• Provides Backend API that allows adding custom backends and pre/post processing operations
• Supports writing custom backends in python, a.k.a. Python-based backends.
• Model pipelines using Ensembling or Business Logic Scripting (BLS)
• HTTP/REST and GRPC inference protocols based on the community developed KServe protocol
• A C API and Java API allow Triton to link directly into your application for edge and other in-process use cases
• Metrics indicating GPU utilization, server throughput, server latency, and more

New to Triton Inference Server? Make use of these tutorials to begin your Triton journey!

Join the Triton and TensorRT community and stay current on the latest product updates, bug fixes, content, best practices, and more. Need enterprise support? NVIDIA global support is available for Triton Inference Server with the NVIDIA AI Enterprise software suite.

Serve a Model in 3 Easy Steps

# Step 1: Create the example model repository
git clone -b r26.03 https://github.com/triton-inference-server/server.git
cd server/docs/examples
./fetch_models.sh

# Step 2: Launch triton from the NGC Triton container
docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:26.03-py3 tritonserver --model-repository=/models --model-control-mode explicit --load-model densenet_onnx

# Step 3: Sending an Inference Request
# In a separate console, launch the image_client example from the NGC Triton SDK container
docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:26.03-py3-sdk /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg

# Inference should return the following
Image '/workspace/images/mug.jpg':
    15.346230 (504) = COFFEE MUG
    13.224326 (968) = CUP
    10.422965 (505) = COFFEEPOT

Please read the QuickStart guide for additional information regarding this example. The quickstart guide also contains an example of how to launch Triton on CPU-only systems. New to Triton and wondering where to get started? Watch the Getting Started video.

Examples and Tutorials

Check out NVIDIA LaunchPad for free access to a set of hands-on labs with Triton Inference Server hosted on NVIDIA infrastructure.

Specific end-to-end examples for popular models, such as ResNet, BERT, and DLRM are located in the NVIDIA Deep Learning Examples page on GitHub. The NVIDIA Developer Zone contains additional documentation, presentations, and examples.

Documentation

Build and Deploy

The recommended way to build and use Triton Inference Server is with Docker images.

• Install Triton Inference Server with Docker containers (Recommended)
• Install Triton Inference Server without Docker containers
• Build a custom Triton Inference Server Docker container
• Build Triton Inference Server from source
• Build Triton Inference Server for Windows 10
• Examples for deploying Triton Inference Server with Kubernetes and Helm on GCP, AWS, and NVIDIA FleetCommand
• Secure Deployment Considerations

Using Triton

Preparing Models for Triton Inference Server

The first step in using Triton to serve your models is to place one or more models into a model repository. Depending on the type of the model and on what Triton capabilities you want to enable for the model, you may need to create a model configuration for the model.

• Add custom operations to Triton if needed by your model
• Enable model pipelining with Model Ensemble and Business Logic Scripting (BLS)
• Optimize your models setting scheduling and batching parameters and model instances.
• Use the Model Analyzer tool to help optimize your model configuration with profiling
• Learn how to explicitly manage what models are available by loading and unloading models

Configure and Use Triton Inference Server

• Read the Quick Start Guide to run Triton Inference Server on both GPU and CPU
• Triton supports multiple execution engines, called backends, including TensorRT, PyTorch, ONNX, OpenVINO, Python, and more
• Not all the above ba