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.

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.

Google's TPU architecture is optimized for internal workloads, such as Search and YouTube, which means their TPU purchases are primarily for their own applications rather than for external sales.

We made these chips programmable via TensorFlow, allowing models to run on CPUs, GPUs, or TPUs with minimal modifications.

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

AI advancements are increasing GPU requirements, as the computational needs for inference are growing significantly.

Our seventh generation TPU, Ironwood, delivers 10x the performance of the previous generation and will be available to Google Cloud customers later this year.

The future of computing will involve running incredibly large neural networks with high performance and low power consumption, requiring different solutions for training and inference workloads.