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

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.

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.

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

Google is confident in its AI advancements, with a focus on bringing deep computer science research into practical products.

Google has always been an AI company at its core, focusing on large-scale data and analysis, which positions it well for the current shift towards AI innovation.