The best ONNX Runtime alternatives

Apache TVM suits developers compiling pre-trained models for varied hardware and runtime targets.

The free plan is described for students, researchers, and hobbyists.

Gradient may suit individual ML/AI engineers, data scientists, researchers, teams, research groups, and startups building or serving machine learning…

TensorFlow suits developers building machine learning models for servers, edge devices, or the web.

MegEngine may suit developers who want a framework spanning model development and deployment, from beginner tutorials to advanced workflows.

PyTorch suits developers, researchers, and enterprises building or deploying AI.

It suits professional Java developers building JVM deep-learning applications, importing models, training across clusters, or deploying to services…

Ludwig suits people building or tuning models across varied data types, from beginners using YAML or auto_train() to experts customizing PyTorch…

SINGA suits teams training deep-learning or machine-learning models who need distributed GPU training or on-premises deployment.

Caffe suits researchers, startups, and industrial teams building deep learning systems with configurable models and local or on-premises training.

MLX is designed for machine learning researchers, particularly those working with Apple silicon.