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Who is Onnx.com?
ONNX (Open Neural Network (NN) Exchange) is an open standard and ecosystem for representing Machine Learning (ML) models to enable interoperability across frameworks, runtimes, and hardware platforms.
- Open standard format for representing ML and deep learning models (AI model interoperability).
- Specification and schema for defining operators, computational graphs, and model metadata (AI model representation).
- ONNX Runtime for high-performance inference across CPUs, GPUs, and specialized accelerators (AI inference runtime).
- Tooling and conversion pipelines to export models from popular training frameworks into the ONNX format (ML framework integration).
- Community-governed ecosystem with contributions from industry and research organizations around model formats, runtimes, and tooling (open-source Artificial Intelligence (AI) infrastructure).
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More About Onnx.com
ONNX (Open NN Exchange) defines an open format for ML models (AI model interoperability) that enables organizations to move models between training frameworks and deployment environments without rewriting graph definitions or operators. The project focuses on a shared, framework-agnostic representation of model structure, operators, and parameters that can be consumed by multiple runtimes and toolchains.
The ONNX specification (AI model representation) describes how computation graphs are represented as nodes and edges, which data types and tensors are supported, and how operators are versioned and organized into operator sets. This structure enables compatibility across multiple versions of frameworks and runtimes, while allowing extension through custom operators when required. The format is commonly used to export models from training environments such as deep learning frameworks into a neutral, deployable artifact.
Onnx.com documents the ONNX Runtime (AI inference runtime), a cross-platform engine designed for running ONNX models on various hardware, including CPUs, GPUs, and other accelerators. ONNX Runtime integrates with hardware-specific execution providers to take advantage of vendor libraries and optimized kernels where available. In enterprise and institutional deployments, this enables a single model format to be executed in different infrastructure environments, from on-premises (on-prem) servers to cloud instances and edge devices, subject to support by the underlying runtime builds.
The ONNX ecosystem (open-source AI infrastructure) also includes tools and libraries for converting models from training frameworks into ONNX format, validating graph structure, and optimizing models for inference. These capabilities support workflows where data scientists train models in their preferred frameworks while platform teams standardize on ONNX for packaging, deployment, and runtime execution. This separation of concerns aligns with common enterprise Machine Learning Operations (MLOps) and model lifecycle practices.
From a marketplace and taxonomy perspective, ONNX can be categorized under AI model interoperability, AI model representation formats, and AI inference runtimes. It is relevant for organizations building AI platforms, inference services, or heterogeneous hardware stacks, where a neutral model format and runtime abstraction reduce coupling between training tools and production infrastructure. The open governance and specification-based approach allow vendors and institutions to implement compatible runtimes, conversion tools, and accelerators around the ONNX standard while maintaining a shared model format for exchange and deployment.
Our description of Onnx.com. Updated February 2026.
