This tech giant has unveiled Ironwood, its latest AI chip dubbed “the most powerful and energy-efficient to date,” promising a tenfold leap in performance efficiency for large-scale inference and model training.
Announced by Google Cloud executives Amin Vahdat and Mark Lohmeyer, the Ironwood TPU is engineered for the most demanding workloads, marking what Google calls the dawn of the “Inference Era.”
Inference Emerges as AI’s New Battleground
Google sees this shift as an industry inflection point—moving from teaching AI models to running them around the clock. In this “Inference Era,” the focus centers on performance, responsiveness, and seamless coordination between general-purpose computing and machine learning accelerators.
As models evolve to handle real-time inference and decision-making, Google asserts that the next breakthrough will stem from system-level design—not just larger datasets or more complex architectures. This philosophy underpins Ironwood: a chip built to power dynamic AI.
Pushing AI Performance to New Frontiers
Google’s new Ironwood TPU is designed to tackle the heaviest AI workloads—from massive model training to rapid inference—redefining its silicon portfolio with dramatic gains in speed and efficiency.
The chip delivers 10x the peak performance of the TPU v5p and over 4x the per-chip performance of its predecessor, Trillium (v6e), making it Google’s most advanced processor yet for AI model training and serving.
Built with enhanced cooling, reliability, and power efficiency, Ironwood is optimized for “planetary-scale” deployments, capable of scaling across thousands of chips without compromising stability.
Early adopters are already validating these claims. Anthropic plans to deploy up to one million TPUs to serve its Claude models, while Lightricks and Essential AI report significant improvements in generation quality and training efficiency.
James Bradbury, Head of Compute at Anthropic, stated: “Ironwood’s advancements in inference performance and training scalability will enable us to scale efficiently while maintaining the speed and reliability our customers expect.”
9,000 Chips Thinking as One
Ironwood doesn’t operate in isolation—it serves as the core of Google’s AI supercomputer, a system where thousands of processors work in concert.
Each super node connects up to 9,216 TPUs via a 9.6-terabit-per-second network, enabling near-instant communication and unified system operation. These nodes share 1.77 petabytes of ultra-high-speed memory, eliminating data bottlenecks that typically hinder large-scale AI processing.
In practice, this means massive models—such as chatbots, image generators, or research systems—can run faster, more efficiently, and without interruption. By enabling thousands of chips to collaborate seamlessly, Google delivers lower latency, quicker responses, and smoother performance for enterprises and developers leveraging its AI infrastructure.
To keep this vast network running smoothly, Google relies on optical circuit switching—a self-healing fabric that instantly redistributes workloads during disruptions. The company reports 99.999% uptime across its fleet since 2020, thanks to advanced liquid cooling and automated cluster management.
A co-designed software stack—including Kubernetes cluster managers, MaxText, vLLM, and the GKE Inference Gateway—squeezes every ounce of performance from the hardware, reducing latency and lowering service costs for customers operating at planetary scale.
Axion Steps In Where Power Meets Practicality
Alongside Ironwood, Google introduced Axion, its new Arm-based CPU family designed to power the everyday compute tasks that keep AI systems running smoothly. The lineup includes the N4A (now in preview) and the upcoming C4A Metal, both engineered to deliver up to 2x better price-performance than comparable x86 virtual machines.
In simple terms, they promise more computing power at lower cost and energy consumption, making it easier and more affordable for businesses to run the supporting tasks AI depends on—from data processing and analytics to application hosting and system management.
Companies testing Axion are already seeing tangible benefits. Vimeo reported a 30% boost in video transcoding performance, ZoomInfo measured a 60% improvement in price-performance for core data workloads, and Rise noted a 20% reduction in compute consumption while maintaining low latency and strong margins.
Together, Ironwood and Axion form a powerful one-two punch for Google: raw acceleration for large-scale AI paired with highly efficient general-purpose computing for everything around it. This full-stack strategy is built for a future where intelligence never sleeps—and the cloud itself learns to think faster.
