# Deployment to i.MX 93

**eIQ AI Toolkit** enables deployment of machine learning models to devices by converting them into a format optimized
for NPU-based inference acceleration.

For **i.MX 93**, an additional conversion step is required. The output remains a quantized TFLite model but is adapted
specifically for execution on the NPU.

To complete the conversion process, refer to these guides:

1. [Deploying machine learning models to devices](./pytorch.ipynb) – Learn the workflow for converting models to
   quantized TF Lite.
2. [Converting model for i.MX 93 NPU](../convQuant/imx93.ipynb) – Perform the final conversion for i.MX 93.