Google AI Infrastructure PM On New TPUs, Liquid Cooling and More

05/13/25 • 19 min

At Google Cloud Next '25, the company introduced Ironwood, its most advanced custom Tensor Processing Unit (TPU) to date. With 9,216 chips per pod delivering 42.5 exaflops of compute power, Ironwood doubles the performance per watt compared to its predecessor. Senior product manager Chelsie Czop explained that designing TPUs involves balancing power, thermal constraints, and interconnectivity.

Google's long-term investment in liquid cooling, now in its fourth generation, plays a key role in managing the heat generated by these powerful chips. Czop highlighted the incremental design improvements made visible through changes in the data center setup, such as liquid cooling pipe placements. Customers often ask whether to use TPUs or GPUs, but the answer depends on their specific workloads and infrastructure. Some, like Moloco, have seen a 10x performance boost by moving directly from CPUs to TPUs. However, many still use both TPUs and GPUs. As models evolve faster than hardware, Google relies on collaborations with teams like DeepMind to anticipate future needs.

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Learn more from The New Stack about the latest AI infrastructure insights from Google Cloud:

Google Cloud Therapist on Bringing AI to Cloud Native Infrastructure

A2A, MCP, Kafka and Flink: The New Stack for AI Agents

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Google Cloud Therapist on Bringing AI to Cloud Native Infrastructure

At Google Cloud Next, Bobby Allen, Group Product Manager for Google Kubernetes Engine (GKE), emphasized GKE’s foundational role in supporting AI platforms. While AI dominates current tech conversations, Allen highlighted that cloud-native infrastructure like Kubernetes is what enables AI workloads to function efficiently. GKE powers key Google services like Vertex AI and is trusted by organizations including DeepMind, gaming companies, and healthcare providers for AI model training and inference.

Allen explained that GKE offers scalability, elasticity, and support for AI-specific hardware like GPUs and TPUs, making it ideal for modern workloads. He noted that Kubernetes was built with capabilities—like high availability and secure orchestration—that are now essential for AI deployment. Looking forward, GKE aims to evolve into a model router, allowing developers to access the right AI model based on function, not vendor, streamlining the development experience. Allen described GKE as offering maximum control with minimal technical debt, future-proofed by Google’s continued investment in open source and scalable architecture.

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Your AI Coding Buddy Is Always Available at 2 a.m.

Aja Hammerly, director of developer relations at Google, sees AI as the always-available coding partner developers have long wished for—especially in those late-night bursts of inspiration. In a conversation with Alex Williams at Google Cloud Next, she described AI-assisted coding as akin to having a virtual pair programmer who can fill in gaps and offer real-time support.

Hammerly urges developers to start their AI journey with tools that assist in code writing and explanation before moving into more complex AI agents. She distinguishes two types of DevEx AI: using AI to build apps and using it to eliminate developer toil. For Hammerly, this includes letting AI handle frontend work while she focuses on backend logic. The newly launched Firebase Studio exemplifies this dual approach, offering an AI-enhanced IDE with flexible tools like prototyping, code completion, and automation. Her advice? Developers should explore how AI fits into their unique workflow—because development, at its core, is deeply personal and individual.

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