Google’s Ironwood TPU: A Quantum Leap in AI Computing Power
The landscape of artificial intelligence has been redefined with Google’s unveiling of its seventh-generation Tensor Processing Unit (TPU), christened Ironwood. This cutting-edge AI accelerator boasts computational prowess that dwarfs even the world’s most formidable supercomputers. In a large-scale deployment, Ironwood’s capabilities exceed those of the fastest supercomputer by a staggering 24 times.
The unveiling of Ironwood at the Google Cloud Next ‘25 event marks a pivotal moment in Google’s decade-long pursuit of AI chip innovation. While previous TPU iterations primarily catered to the training and inference workloads of AI models, Ironwood stands out as the first chip meticulously crafted and optimized for inference tasks.
According to Amin Vahdat, Vice President and General Manager of Machine Learning, Systems, and Cloud AI at Google, ‘Ironwood is engineered to propel the next phase of generative AI, addressing its immense computational and communication demands. We are entering what we call the ‘Inference Era,’ where AI agents will proactively retrieve and generate data to collaboratively deliver insights and answers, surpassing the capabilities of mere data processing.’
Unleashing Unprecedented Computational Power: A Dive into Ironwood’s Capabilities
Ironwood’s technical specifications read like a wish list for AI researchers and developers. Scaling to a pod of 9,216 chips, Ironwood delivers an astounding 42.5 exaflops of AI compute. To put this into perspective, it vastly surpasses the capabilities of the current reigning supercomputer champion, El Capitan, which peaks at 1.7 exaflops. Individually, each Ironwood chip boasts a peak compute capacity of 4614 TFLOPs.
Beyond raw processing power, Ironwood introduces significant enhancements in memory and bandwidth. Each chip is equipped with 192GB of High Bandwidth Memory (HBM), a six-fold increase compared to the previous generation TPU, Trillium. The memory bandwidth has also been dramatically improved, reaching 7.2 terabits/s per chip, 4.5 times that of Trillium.
In an era where data centers are expanding and power consumption is becoming an increasingly critical factor, Ironwood demonstrates remarkable energy efficiency. Its performance per watt is twice that of Trillium and nearly 30 times better than the initial TPU introduced in 2018.
This shift towards inference optimization represents a significant milestone in the evolution of AI. In recent years, leading AI labs have focused on building foundation models with ever-expanding parameter counts. Google’s emphasis on inference optimization signals a shift towards prioritizing deployment efficiency and real-world inference capabilities.
While AI model training is a relatively infrequent activity, inference operations occur billions of times daily as AI technologies become more pervasive. The economic viability of AI-powered businesses is intrinsically linked to inference costs, especially as models become increasingly complex.
Over the past eight years, Google’s demand for AI compute has grown exponentially, increasing tenfold and reaching an astounding 100 million. Without specialized architectures like Ironwood, Moore’s Law alone cannot sustain this growth trajectory.
Google’s emphasis on ‘reasoning models’ capable of complex inference tasks, rather than simple pattern recognition, is particularly noteworthy. This suggests that Google envisions a future where AI excels not only through larger models but also through models capable of breaking down problems, performing multi-step reasoning, and emulating human-like thought processes. The focus on reasoning models signifies a significant evolution in AI development, moving beyond mere data processing and towards more sophisticated cognitive capabilities.
Powering the Next Generation of Large Language Models
Google positions Ironwood as the bedrock infrastructure for its most advanced AI models, including Gemini 2.5, which boasts ‘native reasoning capabilities.’ This signifies the capability of Gemini 2.5 to perform complex problem-solving and decision-making, mimicking human-like thought processes. The combination of Ironwood’s computational power and Gemini 2.5’s reasoning capabilities paves the way for more sophisticated and effective AI applications.
Alongside Ironwood, Google unveiled Gemini 2.5 Flash, a streamlined version of its flagship model designed for latency-sensitive, everyday applications. Gemini 2.5 Flash can dynamically adjust its reasoning depth based on the complexity of the prompt. This adaptability makes it suitable for a wide range of tasks, from simple queries to more complex problem-solving, while maintaining low latency and responsiveness.
Google also showcased its suite of multimodal generative models, encompassing text-to-image, text-to-video, and the newly introduced text-to-music functionality, Lyria. A compelling demo highlighted how these tools can be combined to produce a complete promotional video for a concert. This demonstration highlights the potential of multimodal AI to create rich and engaging content, opening up new possibilities for creative expression and marketing. The seamless integration of text, image, video, and music generation tools signifies a powerful synergy that can transform various industries.
Ironwood is merely one component of Google’s comprehensive AI infrastructure strategy. The company also introduced Cloud WAN, a managed wide area network service that enables businesses to tap into Google’s global-scale private network infrastructure. Cloud WAN provides businesses with a secure and reliable network connection to Google’s AI services, enabling them to leverage the power of AI for their own applications. This underscores Google’s commitment to providing a complete AI ecosystem, from hardware to software to networking.
Google is also expanding its software offerings for AI workloads, including Pathways, a machine learning runtime developed by Google DeepMind, that allows customers to scale model serving across hundreds of TPUs. Pathways enables businesses to efficiently deploy and scale their AI models across a large number of TPUs, maximizing performance and reducing latency. This is crucial for applications that require high throughput and low latency, such as real-time analytics and personalized recommendations.
A Vision of Collaborative Intelligence: Introducing A2A and MCP Support
Beyond hardware advancements, Google articulated its vision for AI centered around multi-agent systems and introduced the Agent-to-Agent (A2A) protocol, designed to foster secure and standardized communication between diverse AI agents. This represents a paradigm shift in AI development, moving towards a more collaborative and interconnected ecosystem. A2A enables AI agents to work together to solve complex problems, leveraging their individual strengths and expertise.
Google anticipates 2025 as a transformative year for AI, with generative AI applications evolving from answering single questions to solving complex problems through interconnected agent systems. This prediction suggests that AI will become increasingly integrated into various aspects of our lives, with agents working autonomously and collaboratively to address a wide range of challenges. The ability of AI agents to solve complex problems will depend on their ability to communicate and coordinate their actions effectively.
The A2A protocol enables interoperability across platforms and frameworks, providing AI agents with a common ‘language’ and secure communication channels. Think of it as a network layer for AI agents, simplifying collaboration in complex workflows and enabling specialized AI agents to collectively tackle tasks of varying complexity and duration, thereby enhancing overall capabilities through cooperation. This interoperability is crucial for fostering a vibrant and diverse AI ecosystem, where different agents can seamlessly interact and share information.
How A2A Works
Google has provided a comparative overview of the MCP and A2A protocols:
- MCP (Model Context Protocol): Focuses on tool and resource management.
- Connects agents to tools, APIs, and resources through structured input/output.
- Google ADK supports MCP tools, facilitating seamless interaction between MCP servers and agents.
- A2A (Agent2Agent Protocol): Facilitates collaboration between agents.
- Enables dynamic, multi-modal communication between agents without requiring shared memory, resources, or tools.
- It is an open standard driven by the community.
- Examples can be explored using tools like Google ADK, LangGraph, and Crew.AI.
A2A and MCP are complementary. MCP equips agents with tools, while A2A empowers these equipped agents to converse and collaborate. MCP ensures that agents have access to the necessary resources, while A2A enables them to work together effectively. This combination is essential for creating intelligent and capable AI systems.
Google’s initial list of partners suggests that A2A is poised to receive similar attention to MCP. The initiative has already attracted over 50 organizations, including leading technology companies and global consulting and system integration providers. This strong industry support underscores the importance and potential of A2A as a key enabler of collaborative AI.
Google emphasizes the protocol’s openness, positioning it as a standard for inter-agent collaboration that transcends underlying technology frameworks or service providers. This commitment to openness is crucial for fostering a level playing field and encouraging innovation in the AI space. By making A2A an open standard, Google aims to ensure that it can be adopted and used by a wide range of developers and organizations.
Google highlighted five guiding principles that shaped the protocol’s design:
Embrace Agent Capabilities: A2A prioritizes enabling agents to collaborate naturally, even without sharing memory, tools, or context. The goal is to enable true multi-agent scenarios, not simply limiting agents to acting as ‘tools.’ This principle reflects the vision of AI agents as autonomous entities with their own capabilities and expertise.
Build on Existing Standards: The protocol leverages existing, widely adopted standards, including HTTP, SSE, and JSON-RPC, simplifying integration with existing IT stacks. This approach makes it easier for developers to adopt A2A and integrate it into their existing systems.
Secure by Default: A2A is designed to support enterprise-grade authentication and authorization, comparable to OpenAPI’s authentication schemes. Security is a paramount concern in the development of AI systems, and A2A addresses this concern by incorporating robust security mechanisms.
Support Long-Running Tasks: A2A’s flexibility allows it to support a wide range of scenarios, from quick tasks to in-depth research that may take hours or even days (especially when human involvement is needed). Throughout the process, A2A can provide users with real-time feedback, notifications, and status updates. This capability is essential for complex tasks that require sustained effort and collaboration.
Modality Agnostic: Recognizing that the world of agents extends beyond text, A2A supports various modalities, including audio and video streams. This multimodal support enables AI agents to interact with the world in a more natural and intuitive way.
Google provided an example of how A2A streamlines the hiring process.
In a unified interface like Agentspace, a hiring manager can assign an agent to identify suitable candidates based on job requirements. This agent can interact with specialized agents to source candidates. Users can also instruct agents to schedule interviews and engage other specialized agents to assist with background checks, enabling fully automated and intelligent recruitment across systems. This example demonstrates the potential of A2A to transform various industries by automating and streamlining complex workflows.
Embracing the Model Context Protocol (MCP)
Google is also embracing MCP. Shortly after OpenAI announced its adoption of Anthropic’s Model Context Protocol (MCP), Google followed suit. This highlights the growing recognition of MCP as an important standard for connecting language models with tools and data.
Demis Hassabis, CEO of Google DeepMind, announced on X (formerly Twitter) that Google would add support for MCP in its Gemini models and SDK, though he did not provide a specific timeline.
Hassabis stated that ‘MCP is an excellent protocol that is rapidly becoming an open standard for the age of AI agents. We look forward to working with the MCP team and other partners in the industry to advance this technology.’ This statement underscores Google’s commitment to supporting open standards and collaborating with the AI community.
Since its release in November 2024, MCP has gained significant traction as a simple, standardized way to connect language models with tools and data. The rapid adoption of MCP reflects its value in simplifying the integration of AI models with real-world applications.
MCP enables AI models to access data from enterprise tools and software to complete tasks and access content libraries and application development environments. The protocol allows developers to establish bidirectional connections between data sources and AI-powered applications such as chatbots. This bidirectional communication enables AI models to learn from data and improve their performance over time.
Developers can expose data interfaces through MCP servers and build MCP clients (such as applications and workflows) to connect to these servers. Since Anthropic open-sourced MCP, several companies have integrated MCP support into their platforms. This open-source approach has fostered innovation and accelerated the adoption of MCP across the industry.
Ironwood: The Dawn of a New Era in AI
Google’s Ironwood TPU represents a significant leap forward in AI computing. Its unprecedented performance, optimized architecture, and support for emerging protocols like A2A and MCP position it as a key enabler of the next wave of AI innovation. The combination of Ironwood’s raw power and the collaborative capabilities enabled by A2A and MCP will unlock new possibilities for AI and transform various industries.
As AI models grow more complex and demanding, Ironwood provides the raw power and flexibility needed to unlock new possibilities and transform industries across the globe. It’s not just a new chip; it’s a foundation for a future powered by intelligent machines working collaboratively to solve complex problems and improve our lives. The development of Ironwood and the associated protocols reflects a long-term vision for AI as a powerful tool for solving some of the world’s most pressing challenges.