TPUs are built for the large matrix calculations at the heart of neural networks. Google first used them internally and later offered them through Google Cloud. They are connected in large groups, called pods, so that many chips can work on one model together. Google has released several generations, each aimed at faster training and more efficient inference.
They matter commercially because AI workloads are very expensive to run. A processor designed for the job can reduce cost and energy use compared with general-purpose chips, and it gives a cloud provider an alternative to relying only on outside suppliers such as Nvidia.
They also have limits. TPUs are tied to Google's software and cloud, so moving a workload to or from them takes effort, and not every model or tool works equally well on them.
When you choose hardware, test your own workload, compare cost per result and not only raw speed, and consider how easy it would be to switch later.