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ONNX Runtime

ONNX Runtime is an open-source C++ runtime and acceleration layer for machine-learning inference and training across supported operating systems, hardware environments and model ecosystems.

Independent OpenViST project profile, based on linked sources.

ONNX Runtime — 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 it when the main requirement is faster or more portable model execution, or when an existing PyTorch transformer training workflow targets multi-node NVIDIA GPUs.

Who it is for

  • Machine-learning application developers
  • Teams building self-hosted model-serving components
  • Engineers integrating supported models into cross-platform or edge applications
  • PyTorch teams training transformer models on multi-node NVIDIA GPUs

Documented uses

  • Run supported machine-learning models through an inference runtime.
  • Apply graph optimizations and available hardware acceleration.
  • Add a portable serving layer to an application or self-hosted system.
  • Accelerate eligible transformer training workflows built with PyTorch.
Before you choose

Requirements & limits

The project is publicly available under the MIT License. The supplied material does not specify account, pricing or service-availability requirements.

  • Whether the target model is supported by the relevant ONNX Runtime path.
  • Whether the selected operating system, drivers and hardware have an applicable execution provider.
  • Whether graph optimization or hardware acceleration addresses the application's execution needs.
  • Whether training uses transformer models, PyTorch and multi-node NVIDIA GPUs for the documented training scenario.
  • Whether the team needs an infrastructure runtime rather than a model-development or user-facing application layer.

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

COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
ONNX RuntimeRun supported machine-learning models through an inference runtime.Android, Cloud, iOS, LinuxThe project is publicly available under the MIT License. The supplied material does not specify account, pricing or service-availability requirements.
MorphingdbImport a libtorch model and create predictions from values in a PostgreSQL table.Docker, Linux, Self-hosted, ServerThe README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension.
Gateway — pyadavSend chat-completion requests to a provider through a common server endpoint.ServerThe repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
1 October 2026
Original website title
microsoft/onnxruntime · Official homepage title · retained in its source language
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