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

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.

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

Google's infrastructure advantage allows it to deliver AI models at a cost-effective price point, enhancing performance and product quality.

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

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

Google has open-sourced the Gemma model, which is designed to leverage the massive matrix-multiply unit of TPUs, resulting in a vocabulary size that is larger than necessary for its model size.

The largest AI models, like GPT-4, require massive GPU clusters, with power consumption reaching unprecedented levels, highlighting the increasing scale of AI infrastructure.