Introducing Ironwood – Google’s Seventh-Generation AI Accelerator
At its annual Cloud Next conference, Google introduced its most advanced AI accelerator to date—Google Ironwood, the company’s seventh-generation Tensor Processing Unit (TPU). Unlike previous generations that focused heavily on training large AI models, Ironwood is purpose-built for inference, the process of running and applying trained models. The chip is scheduled to be made available to Google Cloud customers later in 2025, with two configurations: a 256-chip cluster and a larger 9,216-chip cluster.
“Ironwood is our most powerful, capable, and energy-efficient TPU yet,” stated Amin Vahdat, Vice President at Google Cloud, in a blog post. “It’s designed specifically to scale inferential AI workloads across a wide range of applications.”
With the surge in demand for efficient AI model deployment, particularly in fields like search, recommendation engines, and generative AI, the Ironwood chip marks a critical step forward in how cloud services power large-scale intelligent systems.
Performance and Efficiency Take Center Stage
Google claims Ironwood delivers 4,614 teraflops (TFLOPs) of computing power at its peak performance. Each TPU comes with 192 GB of dedicated RAM and offers a data transfer bandwidth nearing 7.4 terabits per second (Tbps). These specs suggest a significant leap in capability over previous generations, especially in terms of energy efficiency and computing throughput.
One standout feature of Google Ironwood is SparseCore, a specialized processing unit integrated into the chip to handle sparse data workloads commonly seen in advanced recommendation systems. This makes it ideal for use cases like content ranking and personalized suggestions, such as those seen in e-commerce or streaming platforms.
Google emphasized that Ironwood’s architecture has been redesigned to reduce data movement and latency within the chip, which contributes to its energy efficiency—a key focus as the AI industry wrestles with growing power demands from increasingly large models.
Rising Competition in the AI Chip Race
Google Ironwood arrives amid intense competition in the AI chip market, where Nvidia currently leads, but rival tech giants are quickly advancing. Amazon, for instance, has introduced chips like Trainium, Inferentia, and Graviton, all available through AWS. Meanwhile, Microsoft is deploying its Maia 100 AI chip via Azure, as part of its growing in-house hardware strategy.
Google intends to integrate Ironwood into its AI Hypercomputer, a modular and scalable computing cluster in Google Cloud designed to support intensive AI operations. This move signals the company’s ambition to deliver a complete AI infrastructure stack, from custom silicon to cloud deployment.
“Ironwood represents a unique breakthrough in the age of inference,” said Vahdat. “With its increased computing power, expanded memory capacity, networking advancements, and reliability, it sets a new bar for what AI hardware can achieve.”
As the race for AI dominance accelerates, Google Ironwood positions Google as a formidable player not only in cloud services but also in custom AI hardware innovation.
