Google unveils Ironwood, its 7th Gen TPU, to rival Nvidia

Google is significantly upping the ante in the AI infrastructure race with the broad availability of its most powerful custom silicon to date. The tech giant announced Thursday that its seventh-generation Tensor Processing Unit (TPU), codenamed Ironwood, is now generally available to its cloud customers. This follows an initial testing and deployment phase that began in April, marking a pivotal step in Google’s strategy to capture a larger share of the burgeoning AI compute market.

Ironwood, a decade in the making, is an in-house designed accelerator specifically architected for the demands of modern AI workloads. This includes everything from training massive large language models (LLMs) to powering low-latency, real-time inference for chatbots and AI agents. Google boasts that Ironwood’s architecture allows for the interconnection of up to 9,216 TPUs in a single “pod,” effectively eliminating data bottlenecks that often plague demanding AI models. This scale, according to Google, gives customers the computing horsepower to train and deploy the largest, most data-intensive AI models with unprecedented speed and efficiency.

This move positions Google directly against Nvidia, whose GPUs have become the de facto standard for many AI workloads. While GPUs offer general-purpose computing power, Google’s TPUs, as custom silicon, are optimized for the specific mathematical operations that underpin deep learning. This specialization can translate into significant advantages in terms of performance, power consumption, and ultimately, cost-effectiveness for specific AI tasks. The key takeaway here is that Google is aiming to offer a specialized, high-performance alternative to the general-purpose GPU approach.

The competition in the AI infrastructure arena is heating up, with Microsoft, Amazon, and Meta also investing heavily in their own custom silicon and cloud-based AI services. Google claims Ironwood is more than four times faster than its predecessor, and the company has already secured significant commitments. AI startup Anthropic, known for its Claude AI assistant, has pledged to utilize up to 1 million Ironwood TPUs to power and scale its models. This validates Google’s strategy and demonstrates the appeal of custom silicon for specific AI applications.

Beyond the Ironwood chip itself, Google is implementing a series of cloud infrastructure upgrades designed to make its cloud platform more competitive against established players like Amazon Web Services (AWS) and Microsoft Azure. These upgrades focus on improving cost efficiency, accelerating performance, and providing greater flexibility to customers. This holistic approach is crucial, as AI workloads often require a complex combination of compute, storage, and networking resources.

Google’s recent Q3 earnings report highlighted the strong growth in its cloud business, with revenue reaching $15.15 billion, a 34% increase year-over-year. While Azure’s revenue growth outpaced Google’s at 40%, and AWS reported a respectable 20% increase, Google emphasized the increasing number of billion-dollar cloud deals it has secured, exceeding the total from the previous two years combined. To capitalize on this surging demand, Google has increased its capital expenditure forecast for the year to $93 billion, reflecting the massive investments required to build and scale its AI infrastructure.

“We are seeing substantial demand for our AI infrastructure products, including TPU-based and GPU-based solutions,” CEO Sundar Pichai stated during the earnings call. “It is one of the key drivers of our growth over the past year, and I think on a going-forward basis, I think we continue to see very strong demand, and we are investing to meet that.” The availability of Ironwood is a direct response to this demand and underscores Google’s commitment to becoming a leading provider of AI infrastructure. The success of Ironwood will hinge on its ability to deliver tangible benefits in terms of performance, cost, and ease of use compared to alternative solutions, ultimately shaping the future of AI development and deployment.