The TPU program was initiated to meet the demand for high compute power for inference and training in AI applications.

Google primarily uses its own TPUs for AI models but also supports Nvidia chips, balancing performance and computational needs.

Google is committed to both TPUs and GPUs, leveraging both for training and serving AI models, while maintaining flexibility in their infrastructure.

Google Cloud will offer a virtual machine with a 180 teraflop TPU version 2 device, running TensorFlow programs quickly.

We're experimenting with reinforcement learning to optimize the placement of TensorFlow operations across multiple GPUs for better performance.

The adaptation of compute platforms is essential for running capable models in low power environments, such as smartphones and autonomous vehicles, as well as in large-scale data centers.

Our high-performance chip system, called a pod, consists of 256 chips capable of 11.5 petaflops of compute, enabling us to tackle larger problems.

Google has the largest TPU cluster with a very advanced infrastructure for their Tensor Processing Units (TPUs), which are tightly connected across multiple data centers in regions like Nebraska and Iowa, allowing for efficient training of models like GPT-5, but their data centers are spread across multiple locations, complicating multi-datacenter training compared to tightly connected clusters.

We have developed a self-serve platform that allows developers to easily tune AI applications for speed and quality, facilitating rapid scaling and experimentation.