Edge computing means processing data near where it's generated — on a phone, a camera, a car, a factory sensor — instead of sending everything to a distant data center and waiting for a response. For anything time-sensitive, that round trip can be too slow to be useful.
A self-driving car can't afford a network delay to decide whether to brake for a pedestrian, and a factory robot can't wait on a slow connection to stop before an accident. Running a smaller, optimized AI model directly on the device removes that delay entirely, at the cost of using a less powerful model than a full data center could run.
The practical trend is a split: heavy, less time-sensitive AI work — like training a model in the first place — still happens in the cloud, while fast, on-the-spot decisions increasingly happen at the edge, on the device itself.