Google's Training Supercomputers from TPU v2 to Ironwood: Architectural Stability, Scale, Resilience, Power Efficiency, and Sustainability Across Five Generations
This paper provides a comprehensive overview of Google's TPU generations for AI training, highlighting architectural stability and scalability for rapidly evolving workloads like Transformers.
Google's TPU evolution from v2 to Ironwood achieved a 3600x increase in supercomputer performance over eight years, with 10x HBM capacity and bandwidth per node and 100x peak node performance, while improving power efficiency and sustainability.
This paper (to appear in the July/August 2026 issue of IEEE Micro magazine) summarizes five generations of Google s TPUs, from TPU v2 to Ironwood, highlighting their evolution as scalable, resilient, power-efficient, sustainable supercomputers for AI training. It details the TPU s stable architecture, which has surprisingly easily accommodated the rapidly changing deep neural network workloads, such as the rise of Transformers. Key advancements over eight years include 10x increase in HBM capacity and bandwidth per node, a 100x increase in peak node performance, and a 3600x increase in supercomputer performance. The paper also discusses the role of optical circuit switches, built-in self test, and hardware replay in enhancing resilience and how TPU's environmental impact is reduced with substantial improvements in performance per Watt and in carbon emissions per floating point operation. It concludes by identifying six features that may well characterize successful training accelerators of this decade.