Google develops and operates AI infrastructure across its own products, its cloud platform, and its custom silicon program. The company has been designing Tensor Processing Units (TPUs) since 2016—custom AI chips optimized for the matrix multiplication operations at the core of neural network computation—and has used them internally to run AI workloads at efficiency levels not achievable with general-purpose GPUs. TPUs are co-designed with Broadcom and manufactured at TSMC.

Google Cloud has become a primary commercial AI platform, offering TPUs, third-party GPUs, and AI development services to enterprises and AI labs. The commercial AI market has driven Google Cloud revenue growth as companies integrate AI into their workflows using Google's Gemini models and development tools. Google's DeepMind research division continues developing frontier models, including Gemini 2.0, deployed commercially through Google products and APIs.

Google's TPU program has continued to advance rapidly. Google's seventh-generation TPU, Ironwood, debuted as the company's first TPU designed for the age of inference, delivering ten times the peak performance of the TPU v5p and scaling to 9,216 liquid-cooled chips in a superpod producing 42.5 FP8 exaflops. On the supply chain side, Google has moved toward a multi-vendor architecture: Broadcom recently locked in a through-2031 TPU agreement, and Marvell has emerged as a potential third design partner. Google is in talks with Marvell to develop two new chips — a memory processing unit to work alongside existing TPUs, and a new inference-optimized TPU.