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AI Infrastructure & Serving

Triton Inference Server

Triton Inference Server is open-source software that deploys and serves AI models from multiple machine learning and deep learning frameworks across cloud, datacenter, edge and embedded environments.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Triton Inference Server — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Triton when model deployment requires a dedicated serving layer that can host models from multiple frameworks and expose them through standard inference protocols or direct application APIs.

Who it is for

  • AI and machine learning teams deploying models as shared services
  • Platform engineers building self-hosted inference infrastructure
  • Developers integrating model inference into applications through network or in-process APIs
  • Teams serving models on supported GPU, CPU or AWS Inferentia environments

Documented uses

  • Deploy models from supported frameworks behind inference interfaces
  • Serve real-time, batched, ensemble or audio/video streaming workloads
  • Improve serving efficiency with concurrent execution and dynamic or sequence batching
  • Connect multiple models into pipelines with ensembles or Business Logic Scripting
  • Monitor GPU utilisation, server throughput and server latency
Before you choose

Requirements & limits

The project is public and open source under the BSD-3-Clause licence. The repository recommends Docker containers, and its documentation also describes non-Docker builds, custom containers, source builds and Kubernetes or Helm deployment paths.

  • Framework coverage includes TensorRT, PyTorch, ONNX, OpenVINO, Python and RAPIDS FIL.
  • Supported inference targets include NVIDIA GPUs, x86 and ARM CPUs, and AWS Inferentia.
  • HTTP/REST and gRPC interfaces are available, alongside C and Java APIs for in-process use cases.
  • Custom Python backends and a backend API extend model and preprocessing options.
  • The documented deployment paths include Docker, Kubernetes and Helm, but the suitable path depends on the target environment and model setup.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Serve the documented example model with Docker

Set up Triton with the example model repository and send an image inference request.

Before you start

  • Git
  • Docker with GPU support for the GPU example
  • Access to the NVIDIA Triton Server and SDK containers
  • The Triton repository and its example model files

Steps

  1. Clone the repository at the documented r26.09 branch and change to its examples directory.

  2. Run fetch_models.sh to create the example model repository.

  3. Launch the documented Triton 26.09 container with GPU access, host networking and the model repository mounted at /models.

  4. In a separate console, launch the matching SDK container and run the documented image_client example against densenet_onnx.

Expected outcome

The documented example returns image classifications for the supplied mug image.

Guide limitations

  • This recipe follows the GPU container example and does not establish compatibility with other hardware or model configurations.
  • The repository notes that the main branch tracks under-development progress; the commands use the r26.09 branch and 26.09 containers.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
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Sources checked
2 October 2026
Original website title
triton-inference-server/server · Official homepage title · retained in its source language
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