ONNX<\/strong> (Open Neural Network Exchange) is a common format for moving machine-learning models between tools regardless of the framework that created them (such as PyTorch). A Sources: the ONNX website<\/a>, ONNX Runtime documentation<\/a>, the microsoft\/onnxruntime-genai README<\/a> and Transformers.js documentation<\/a> (all as of 2026-09-27).<\/em><\/p>\n Local Model Watch has published 1 article(s) on models available in ONNX: 1 where the repository itself is in ONNX, and 0 where we found a ONNX build of the model. The lists below only include builds we have checked (the publisher’s organization and well-known quantizers); a model missing here may still have a ONNX build elsewhere. Part of our model format index<\/a>.<\/p>\n \u2192 Scroll horizontally to see all columns<\/p>\n.onnx<\/code> file holds the computation graph together with the weights. Models are usually run with Microsoft’s ONNX Runtime<\/a>, and for LLMs there is ONNX Runtime GenAI<\/a>, which packages the generation loop.<\/p>\nWhy It Matters<\/h2>\n
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Tips for Running It Locally<\/h2>\n
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.onnx_data<\/code>) that must sit next to the .onnx<\/code> file.<\/li>\n<\/ul>\nOur Coverage and Data<\/h2>\n
Models Available in ONNX<\/h2>\n