Google Cloud unveiled its seventh-generation Tensor Processing Unit (TPU) referred to as Ironwood on Wednesday, a customized AI accelerator that the corporate claims delivers greater than 24 occasions the computing energy of the world’s quickest supercomputer when deployed at scale.
The brand new chip, introduced at Google Cloud Subsequent ’25, represents a major pivot in Google’s decade-long AI chip improvement technique. Whereas earlier generations of TPUs have been designed primarily for each coaching and inference workloads, Ironwood is the primary purpose-built particularly for inference — the method of deploying skilled AI fashions to make predictions or generate responses.
“Ironwood is built to support this next phase of generative AI and its tremendous computational and communication requirements,” stated Amin Vahdat, Google’s Vice President and Basic Supervisor of ML, Programs, and Cloud AI, in a digital press convention forward of the occasion. “This is what we call the ‘age of inference’ where AI agents will proactively retrieve and generate data to collaboratively deliver insights and answers, not just data.”
Shattering computational obstacles: Inside Ironwood’s 42.5 exaflops of AI muscle
The technical specs of Ironwood are hanging. When scaled to 9,216 chips per pod, Ironwood delivers 42.5 exaflops of computing energy — dwarfing El Capitan‘s 1.7 exaflops, currently the world’s quickest supercomputer. Every particular person Ironwood chip delivers peak compute of 4,614 teraflops.
Ironwood additionally options vital reminiscence and bandwidth enhancements. Every chip comes with 192GB of Excessive Bandwidth Reminiscence (HBM), six occasions greater than Trillium, Google’s previous-generation TPU introduced final 12 months. Reminiscence bandwidth reaches 7.2 terabits per second per chip, a 4.5x enchancment over Trillium.
Maybe most significantly in an period of power-constrained knowledge facilities, Ironwood delivers twice the efficiency per watt in comparison with Trillium, and is sort of 30 occasions extra energy environment friendly than Google’s first Cloud TPU from 2018.
“At a time when available power is one of the constraints for delivering AI capabilities, we deliver significantly more capacity per watt for customer workloads,” Vahdat defined.
From mannequin constructing to ‘thinking machines’: Why Google’s inference focus issues now
The emphasis on inference slightly than coaching represents a major inflection level within the AI timeline. For years, the trade has been fixated on constructing more and more large basis fashions, with firms competing totally on parameter dimension and coaching capabilities. Google’s pivot to inference optimization suggests we’re coming into a brand new section the place deployment effectivity and reasoning capabilities take heart stage.
This transition is smart. Coaching occurs as soon as, however inference operations happen billions of occasions each day as customers work together with AI methods. The economics of AI are more and more tied to inference prices, particularly as fashions develop extra complicated and computationally intensive.
Through the press convention, Vahdat revealed that Google has noticed a 10x year-over-year improve in demand for AI compute over the previous eight years — a staggering issue of 100 million total. No quantity of Moore’s Regulation development might fulfill this development curve with out specialised architectures like Ironwood.
What’s significantly notable is the concentrate on “thinking models” that carry out complicated reasoning duties slightly than easy sample recognition. This implies Google sees the way forward for AI not simply in bigger fashions, however in fashions that may break down issues, cause via a number of steps, and basically simulate human-like thought processes.
Gemini’s pondering engine: How Google’s next-gen fashions leverage superior {hardware}
Google is positioning Ironwood as the muse for its most superior AI fashions, together with Gemini 2.5, which the corporate describes as having “thinking capabilities natively built in.”
On the convention, Google additionally introduced Gemini 2.5 Flash, a cheaper model of its flagship mannequin that “adjusts the depth of reasoning based on a prompt’s complexity.” Whereas Gemini 2.5 Professional is designed for complicated use instances like drug discovery and monetary modeling, Gemini 2.5 Flash is positioned for on a regular basis purposes the place responsiveness is important.
The corporate additionally demonstrated its full suite of generative media fashions, together with text-to-image, text-to-video, and a newly introduced text-to-music functionality referred to as Lyria. An indication confirmed how these instruments might be used collectively to create a whole promotional video for a live performance.
Past silicon: Google’s complete infrastructure technique consists of community and software program
Ironwood is only one a part of Google’s broader AI infrastructure technique. The corporate additionally introduced Cloud WAN, a managed wide-area community service that offers companies entry to Google’s planet-scale personal community infrastructure.
“Cloud WAN is a fully managed, viable and secure enterprise networking backbone that provides up to 40% improved network performance, while also reducing total cost of ownership by that same 40%,” Vahdat stated.
Google can also be increasing its software program choices for AI workloads, together with Pathways, its machine studying runtime developed by Google DeepMind. Pathways on Google Cloud permits clients to scale out mannequin serving throughout tons of of TPUs.
AI economics: How Google’s $12 billion cloud enterprise plans to win the effectivity battle
These {hardware} and software program bulletins come at an important time for Google Cloud, which reported $12 billion in This fall 2024 income, up 30% 12 months over 12 months, in its newest earnings report.
The economics of AI deployment are more and more changing into a differentiating issue within the cloud wars. Google faces intense competitors from Microsoft Azure, which has leveraged its OpenAI partnership right into a formidable market place, and Amazon Internet Providers, which continues to broaden its Trainium and Inferentia chip choices.
What separates Google’s method is its vertical integration. Whereas rivals have partnerships with chip producers or acquired startups, Google has been creating TPUs in-house for over a decade. This provides the corporate unparalleled management over its AI stack, from silicon to software program to providers.
By bringing this know-how to enterprise clients, Google is betting that its hard-won expertise constructing chips for Search, Gmail, and YouTube will translate into aggressive benefits within the enterprise market. The technique is evident: provide the identical infrastructure that powers Google’s personal AI, at scale, to anybody prepared to pay for it.
The multi-agent ecosystem: Google’s audacious plan for AI methods that work collectively
Past {hardware}, Google outlined a imaginative and prescient for AI centered round multi-agent methods. The corporate introduced an Agent Growth Package (ADK) that enables builders to construct methods the place a number of AI brokers can work collectively.
Maybe most importantly, Google introduced an “agent-to-agent interoperability protocol” (A2A) that allows AI brokers constructed on completely different frameworks and by completely different distributors to speak with one another.
“2025 will be a transition year where generative AI shifts from answering single questions to solving complex problems through agented systems,” Vahdat predicted.
Google is partnering with greater than 50 trade leaders, together with Salesforce, ServiceNow, and SAP, to advance this interoperability customary.
Enterprise actuality examine: What Ironwood’s energy and effectivity imply in your AI technique
For enterprises deploying AI, these bulletins might considerably scale back the associated fee and complexity of operating subtle AI fashions. Ironwood’s improved effectivity might make operating superior reasoning fashions extra economical, whereas the agent interoperability protocol might assist companies keep away from vendor lock-in.
The true-world impression of those developments shouldn’t be underestimated. Many organizations have been reluctant to deploy superior AI fashions as a result of prohibitive infrastructure prices and power consumption. If Google can ship on its performance-per-watt guarantees, we might see a brand new wave of AI adoption in industries which have to this point remained on the sidelines.
The multi-agent method is equally vital for enterprises overwhelmed by the complexity of deploying AI throughout completely different methods and distributors. By standardizing how AI methods talk, Google is trying to interrupt down the silos which have restricted AI’s enterprise impression.
Through the press convention, Google emphasised that over 400 buyer tales could be shared at Subsequent ’25, showcasing actual enterprise impression from its AI improvements.
The silicon arms race: Will Google’s customized chips and open requirements reshape AI’s future?
As AI continues to advance, the infrastructure powering it can develop into more and more important. Google’s investments in specialised {hardware} like Ironwood, mixed with its agent interoperability initiatives, recommend the corporate is positioning itself for a future the place AI turns into extra distributed, extra complicated, and extra deeply built-in into enterprise operations.
“Leading thinking models like Gemini 2.5 and the Nobel Prize winning AlphaFold all run on TPUs today,” Vahdat famous. “With Ironwood we can’t wait to see what AI breakthroughs are sparked by our own developers and Google Cloud customers when it becomes available later this year.”
The strategic implications lengthen past Google’s personal enterprise. By pushing for open requirements in agent communication whereas sustaining proprietary benefits in {hardware}, Google is trying a fragile balancing act. The corporate needs the broader ecosystem to flourish (with Google infrastructure beneath), whereas nonetheless sustaining aggressive differentiation.
How shortly rivals reply to Google’s {hardware} developments and whether or not the trade coalesces across the proposed agent interoperability requirements might be key components to look at within the months forward. If historical past is any information, we will count on Microsoft and Amazon to counter with their very own inference optimization methods, probably organising a three-way race to construct essentially the most environment friendly AI infrastructure stack.
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