Edge

Modzy Edge provides a way for machine learning models to run "at the edge" on hardware such as remove servers and edge devices.

How does Modzy Edge Work?

When you connect your edge device to Modzy, the following things happen:

1. It downloads a copy of Modzy Core to your edge device. This small executable provides an API interface for any models you deploy to the edge. You can learn more about the Modzy Edge API here.
2. It registers your device with Modzy and associates it with a device group
3. It automatically downloads any models you've added to your device group and starts running them on your edge device

Edge Hardware Reference

To use Modzy Edge on an edge device, that device must meet some minimum hardware requirements. These requirements are typically easy to meet in the cloud, or on-prem, but Modzy Edge also works with many small form-factor devices.

Minimum Hardware Requirements

Validated Small Form-factor Devices

Modzy Edge has been tested on the following small form-factor edge devices

Hardware Resource Guidance

When using Modzy Edge, an important consideration to make in addition to the edge device meeting the list of minimum requirements is the model's resource footprint. In this context, resource footprint refers to the amount of RAM, CPU cores, and perhaps GPU(s) your model needs to run inferences. This consideration is especially critical when using Modzy Edge on small form-factor edge devices with limited resources (i.e., your device's resources must be able to handle your model's footprint).

Since determining a model's resource footprint is difficult, highly variable, and depends on many factors, we put together a table that provides examples of models and their corresponding footprint details as a reference.

Performance Benchmarks

Modzy Core v1.5

Modzy Core v1.6