onnxruntime/docs/tutorials/iot-edge/index.md

title	parent	has_children	nav_order	redirect_from
Deploy on IoT and edge	Tutorials	true	8	/docs/get-started/with-iot

Deploy ML Models on IoT and Edge Devices

ONNX Runtime allows you to deploy to many IoT and Edge devices to support a variety of use cases. There are packages available to support many board architectures included when you install ONNX Runtime. Below are some considerations when deciding if deploying on-device is right for your use case.

Benefits and limitations to doing on-device inference

• It’s faster. That’s right, you can cut inferencing time down when inferencing is done right on the client for models that are optimized to work on less powerful hardware.
• It’s safer and helps with privacy. Since the data never leaves the device for inferencing, it is a safer method of doing inferencing.
• It works offline. If you lose internet connection, the model will still be able to inference.
• It’s cheaper. You can reduce cloud serving costs by offloading inference to the device.
• Model size limitation. If you want to deploy on device you need to have a model that is optimized and small enough to run on the device.
• Hardware processing limitation. The model needs to be optimized to run on less powerful hardware.

Examples

• Raspberry Pi on Device inference
• Jetson Nano embedded device: Fast model inferencing
• Intel VPU edge device with OpenVINO: Deploy small quantized model