Edge computing means doing the processing close to where data is produced rather than sending it to a central data centre. That is the whole concept, and the reasons for it are practical.
Latency
A signal takes time to travel. For anything requiring a response in milliseconds – industrial control, autonomous vehicles, some interactive applications – a round trip to a distant data centre is too slow regardless of how fast the network is.
Bandwidth
A camera producing continuous high-resolution video generates far more data than is worth transmitting. Processing locally and sending only the interesting parts – a summary, an alert, a frame – reduces the volume enormously.
Reliability
A system that stops working when the connection drops is unacceptable in many settings. Local processing continues regardless.
Privacy and regulation
Data that never leaves the premises is not subject to the concerns that arise when it does. For medical, industrial and personal data this is frequently the deciding factor.
What it looks like in practice
A camera that identifies what it sees on the device rather than uploading video. A factory with local computing controlling machinery. A phone running a model locally instead of calling a service. A content network with servers near users rather than one origin.
The trade-offs
Managing many distributed devices is harder than managing one data centre. Updating them, securing them, and monitoring them is a real operational burden.
And local hardware is less powerful than a data centre, so the processing has to fit.
Where it settles
Hybrid. Time-sensitive and high-volume work at the edge, heavy processing and long-term storage centrally. Which is how most systems are actually built.