How it works
TPU stands for Tensor Processing Unit. According to the official Google Cloud documentation, TPUs are application specific integrated circuits developed by Google to accelerate machine learning workloads. They are designed to run common operations in those workloads.
A TPU is part of an infrastructure that includes memory, network, software and tools to prepare and run the model. The developer needs to use a compatible configuration and organize the workload to take advantage of the accelerator. The result depends on the operation, the data size, the model and the communication between components.
A TPU is not simply a GPU with a different name. They are different types of accelerators, with their own ecosystems and characteristics. There is no universally better choice: model compatibility, software, availability and performance measured in the real case influence the decision.
Practical example
A team wants to run inference for a language model in a cloud environment. First, it checks whether the model and the libraries used support the available accelerator. Then it prepares a representative load, runs tests and compares quality, latency and stability with the alternative already in use.
If the system requires adaptations that increase the work a lot, the team may prefer to keep another infrastructure. It should also confirm how data is sent, stored and protected in the chosen service. The decision should consider the full flow, not just the speed of an isolated operation.
For distributed training, splitting work across devices involves additional configuration and communication. The team should follow the environment documentation and measure the result with its own model before estimating capacity or timeline.
What it is for
TPUs are intended to accelerate compatible machine learning workloads, including training and inference. The Google Cloud documentation describes ways to access TPUs via services such as Compute Engine, Google Kubernetes Engine and Vertex AI; the available option depends on the current product and configuration.
They can be considered when the workload benefits from the hardware and the software already offers adequate support. For a small task, a CPU or another service may be enough. To choose, validate support, memory usage, throughput, latency, operating cost and adaptation effort in the real environment.
Always consult the current official documentation before planning a deployment, because machine types, availability and requirements can change. Start with a small proof of concept, protect the data and establish a fallback option in case the accelerator is not available or the model does not meet the defined criteria.