Edge AI is quietly changing how we think about machine learning deployment. Instead of running models in the cloud, intelligence is moving closer to where data is created—on sensors, gateways, and even microcontrollers. This shift isn’t about shrinking models for fun; it’s about making AI useful where latency, bandwidth, and power all matter. In this piece, we look at how tools like ExecuTorch and TorchScript let you run PyTorch models anywhere… whether that’s a GPU rack, a Raspberry Pi, or a factory floor controller and why efficiency is the next big driver of innovation in AI.

What’s fueling this movement is necessity. As the world races toward smaller, faster, and more sustainable AI systems, the focus has moved from “how big can we make it” to “how far can we take it.” One of the best examples was DeepSeek’s efficiency breakthroughs… it’s clear that doing more with less is the future of machine learning.

Dive into the full article here: https://bit.ly/4mSCFOl.