Introduction to Ironwood
Google has introduced its seventh-generation Tensor Processing Unit (TPU), named Ironwood, at the Google Cloud Next 2025 event. This latest TPU is specifically designed for AI inference, marking a significant leap in AI model processing. By leveraging Ironwood, Google aims to support the growing demand for next-generation AI workloads.
Key Features of Ironwood
Ironwood offers several groundbreaking enhancements over its predecessor, the sixth-generation Trillium TPU:
- Each Ironwood chip delivers a peak compute performance of 4,614 teraflops.
- A single pod configuration can scale to 9,216 chips, offering a collective performance of 42.5 exaflops. This is over 24 times the computing power of the world's fastest supercomputer, El Capitan.
- The new architecture includes 192GB of high-bandwidth memory (HBM) per chip, representing a sixfold increase from Trillium, ensuring faster processing and data transfers.
- Inter-Chip Interconnect (ICI) technology has been enhanced to boost bidirectional bandwidth by 1.5 times, improving chip-to-chip communication efficiency.
Energy Efficiency
Ironwood establishes itself as Google's most energy-efficient TPU yet. Performance per watt has doubled compared to Trillium, achieving significant energy savings, especially critical for high-demand AI inferencing operations.
Support for Thinking Models
The Ironwood TPU is optimized to handle the demands of “thinking models” such as Large Language Models (LLMs) and Mixture of Experts (MoEs). These models require massive parallel processing and efficient memory access to perform advanced reasoning and retrieve insights proactively.
Tackling AI Demands
Google has designed Ironwood to support autonomous AI agents capable of continuously generating data, making it suitable for applications in security, customer service, real-time data insights, and more. The TPU minimizes latency and data movement while processing large-scale AI tasks efficiently.
Pathways Runtime and Scalable AI Infrastructure
To complement Ironwood, Google is making Pathways, its distributed machine learning runtime, available to Cloud customers. Pathways enables dynamic scaling by interconnecting Ironwood pods, creating massive compute clusters with low latency and high throughput. This innovation will allow customers to deploy frontier AI models more effectively.
Broader AI Infrastructure Enhancements
Google has also integrated Ironwood into its comprehensive AI infrastructure stack. In addition to TPUs, Google Cloud customers can access other accelerator hardware, including Nvidia GPUs, ensuring flexibility and performance across diverse AI workloads.
Conclusion
The launch of the Ironwood TPU underscores Google’s commitment to advancing AI technology and infrastructure. With its unparalleled performance, efficiency, and scalability, Ironwood is poised to empower enterprises to unlock the full potential of generative AI applications.
