---
title: Deploy on IoT and edge
parent: Tutorials
has_children: true
nav_order: 8
redirect_from: /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](https://pypi.org/project/onnxruntime/#files). 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](rasp-pi-cv.md)
* [Jetson Nano embedded device: Fast model inferencing](https://github.com/Azure-Samples/onnxruntime-iot-edge/blob/master/README-ONNXRUNTIME-arm64.md)
* [Intel VPU edge device with OpenVINO: Deploy small quantized model](https://github.com/Azure-Samples/onnxruntime-iot-edge/blob/master/README-ONNXRUNTIME-OpenVINO.md)