ONNX Runtime

Cross-platform inference engine for ONNX models by Microsoft.

Open SourceSelf HostedOffline Capable
0.0 (0)

About

ONNX Runtime is Microsoft's cross-platform accelerator for machine learning inference and training. It runs models in the ONNX interchange format exported from deep learning frameworks like PyTorch and TensorFlow or from classical libraries such as scikit-learn, LightGBM, and XGBoost, applying graph optimizations and dispatching to hardware-specific execution providers. Backends cover CPU, NVIDIA GPUs through CUDA and TensorRT, DirectML on Windows, and mobile targets, so the same model can move between server, desktop, and edge deployments without rewriting application code. On the training side it accelerates transformer training on multi-node NVIDIA GPU clusters with minimal changes to existing PyTorch scripts. Released under the MIT license, it is widely embedded in production software by teams that need consistent, fast model execution across heterogeneous hardware without tying the application to a single training framework.

Should you use ONNX Runtime?

Pick it when

Pick this when one inference engine has to cover servers, Windows desktops, and mobile, with models exported once from PyTorch, TensorFlow, scikit-learn, LightGBM, or XGBoost and run through CPU, CUDA, TensorRT, or DirectML backends.

Look elsewhere when

High-throughput LLM serving on NVIDIA GPUs is better handled by TensorRT-LLM or vLLM, and Intel-only fleets get more from OpenVINO. If your model will not export cleanly to ONNX, ExecuTorch or plain PyTorch avoids the conversion fight.

Alternatives to ONNX Runtime

  • TensorRT-LLM

    Built for LLM and generative model throughput on NVIDIA with in-flight batching and paged KV cache; locked to NVIDIA GPUs, where this one is a general runtime.

  • OpenVINO

    Targets Intel CPUs, GPUs, and NPUs with GenAI pipelines; the better pick for Intel-heavy fleets, while this one spans NVIDIA, DirectML, and mobile.

  • ExecuTorch

    On-device PyTorch runtime with a roughly 50 KB base footprint and many mobile backends; skips ONNX export but only accepts PyTorch models.

  • Triton Inference Server

    A full serving platform with dynamic batching and ensembles that can host ONNX models; adds a server where this one is an embeddable library.

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Details

Price
Free
Platform
Local/Desktop
Difficulty
Easy (2/5)
License
MIT
Added
Apr 3, 2026

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