Google Has Launches Its Most Powerful AI Chip Yet — IRONWOOD

Google has unveiled Ironwood, its seventh-generation TPU, designed to handle the most demanding AI workloads. The chip delivers unmatched speed, efficiency, and scalability, making it a major step forward in AI infrastructure. Enterprises can now train massive models and run real-time AI applications faster than ever.

With support for up to 9,216 chips per pod, Ironwood sets a new standard in compute power and low-latency performance. Anthropic and other AI leaders are already deploying it, reflecting strong industry confidence. This launch underscores Google’s commitment to custom AI hardware and its dominance in cloud AI services.

About Ironwood: Google’s Seventh-Generation TPU

Google’s Ironwood TPU is the most advanced chip yet. It is custom-built for AI workloads. Enterprises and researchers can now handle massive models efficiently.

Overview of Performance and Capabilities

Ironwood offers unmatched performance across training and inference tasks. Each chip delivers 4,614 TFLOPs, enabling huge parallel computations. This allows developers to train large models in a fraction of the time previous TPUs required. 

The high bandwidth memory and optimized interconnects reduce latency, ensuring data is always available for processing. With its energy-efficient design, Ironwood can sustain heavy workloads for extended periods without overheating. Organizations benefit from cost savings and faster deployment cycles when using Ironwood for advanced AI applications.

Designed for Inference and Generative AI

Ironwood is specifically designed for thinking models that generate insights proactively. It excels at real-time inference, allowing AI agents to provide responses without human intervention. This is critical for generative AI applications like chatbots, recommendation engines, and large language models. 

With low-latency communication across thousands of chips, models can collaborate efficiently. Ironwood’s architecture minimizes data movement, making it ideal for complex tensor operations. Enterprises can leverage these capabilities to build AI-driven solutions faster and at scale.

Unmatched Speed and Efficiency

Ironwood delivers unprecedented speed for AI tasks. It is more than four times faster than previous TPU generations. The chip also reduces energy consumption, making it highly efficient.

4x Faster Performance Over Previous TPUs

Each Ironwood chip is designed for massive parallelism, allowing multiple tasks to run simultaneously. Its optimized tensor cores execute calculations faster than ever before. This speed improvement reduces training times for large AI models dramatically. The TPU pod structure allows thousands of chips to work together seamlessly. 

Developers can now train models that were previously impractical due to compute limitations. Ironwood’s performance also supports real-time AI applications, ensuring results are delivered instantly. For companies handling data-intensive workloads, this chip represents a major competitive advantage.

Power Efficiency and Liquid Cooling

Ironwood uses advanced liquid cooling to maintain optimal temperatures under heavy loads. This allows chips to sustain peak performance without throttling. The design is almost 30 times more power-efficient than Google’s first TPU. 

Lower power usage reduces operational costs for enterprises and supports sustainable AI initiatives. High efficiency also means larger models can be deployed without exceeding energy budgets. Ironwood’s combination of speed and efficiency makes it ideal for AI infrastructure at scale.

Massive Scalability for AI Workloads

Ironwood is built to handle large-scale workloads without compromising performance. It supports pods of up to 9,216 chips, enabling extreme parallel processing.

Up to 9,216 Chips per Pod

A single Ironwood pod can link thousands of chips through a high-speed interconnect. This design eliminates data bottlenecks and ensures synchronous processing. Pods are capable of delivering 42.5 exaflops, surpassing even the world’s largest supercomputers. 

Developers can train dense models or Mixture of Experts architectures efficiently. The ability to scale horizontally allows enterprises to expand AI workloads without redesigning infrastructure. With flexible pod configurations, Ironwood adapts to different computational needs, from mid-sized AI startups to global enterprises.

High Bandwidth Memory and Inter-Chip Communication

Each chip comes with 192 GB HBM, six times more than the previous TPU generation. Memory bandwidth reaches 7.37 TB/s, ensuring fast access to large datasets. Inter-chip communication is enhanced through 1.2 TBps bidirectional ICI, allowing efficient synchronization across thousands of chips. 

This minimizes latency in distributed AI training, which is essential for modern generative models. Ironwood’s architecture reduces overhead for large embeddings and complex tensor operations. By keeping data close to the compute units, AI models run faster and more reliably at massive scales.

Pathways Software and Distributed Computing

Ironwood works seamlessly with Google’s Pathways software, designed for distributed AI. It simplifies the management of tens of thousands of TPUs for large models.

Leveraging Tens of Thousands of TPUs

Pathways enables developers to scale AI models across multiple pods without complex infrastructure changes. It distributes workloads intelligently, ensuring balanced compute and reducing idle time. 

Large language models and Mixture of Experts architectures can operate efficiently across hundreds of thousands of cores. Pathways also handles fault tolerance, so interruptions in one chip do not halt overall processing. Enterprises benefit from simplified deployment of massive AI models with predictable performance.

Simplifying Large-Scale AI Model Training

Training models with hundreds of billions of parameters requires precise coordination between chips. Pathways abstracts this complexity, letting developers focus on model design and innovation. Ironwood’s hardware, combined with Pathways, reduces communication latency and ensures optimal memory usage. 

This results in faster training cycles and reliable inference performance. Companies can now experiment with state-of-the-art AI models without infrastructure constraints. The combination of Ironwood and Pathways makes Google Cloud an ideal platform for cutting-edge AI research and enterprise deployment.

SparseCore Accelerator for Advanced Workloads

Ironwood includes the SparseCore accelerator, designed for advanced workloads. It speeds up embedding computations and reduces memory overhead. This makes AI models more efficient and scalable.

Optimizing Large Embeddings

SparseCore focuses on handling ultra-large embeddings, which are common in recommendation systems and ranking algorithms. The accelerator processes these embeddings faster, reducing training time for large models. Its design minimizes data movement, which is critical for memory-intensive workloads. 

By offloading embedding operations to SparseCore, developers can use larger models without facing hardware limitations. The accelerator also improves latency performance, ensuring AI applications respond in real time. This makes Ironwood ideal for next-generation AI tasks that require complex data manipulation.

Applications Beyond Traditional AI

SparseCore is not limited to conventional AI workloads. It supports financial modeling, scientific simulations, and other high-computation tasks. Enterprises can deploy Ironwood for complex analyses without redesigning infrastructure. Its efficiency enables cost-effective scaling across multiple pods. 

SparseCore also accelerates graph-based computations and knowledge retrieval tasks. This allows companies to explore new AI applications in domains previously constrained by hardware. Ironwood’s flexibility ensures a wide range of innovative AI solutions can be deployed reliably.

Industry Adoption and Early Deployments

Ironwood is seeing strong adoption across AI companies. Enterprises are deploying it for large-scale models and real-time applications. Demand is growing rapidly.

Anthropic’s Deployment of 1 Million Ironwood TPUs

Anthropic plans to deploy up to 1 million Ironwood TPUs to run its Claude AI models. This massive deployment demonstrates industry confidence in Google’s hardware. With Ironwood, Anthropic can scale training and inference simultaneously across multiple regions. The TPU pods reduce latency issues and improve overall efficiency. 

Anthropic’s use case highlights the chip’s ability to handle data-intensive workloads at a global scale. It also shows how Ironwood can support cutting-edge generative AI, enabling new levels of performance and reliability.

Other AI Startups and Enterprise Use Cases

Beyond Anthropic, multiple startups and enterprises are testing Ironwood for AI workloads. These include Lightricks and Essential AI, focusing on image, video, and text generation. Ironwood enables rapid model iteration, helping companies deploy solutions faster. 

Its scalability allows organizations to experiment with large models without worrying about infrastructure constraints. Enterprises benefit from predictable performance and lower energy costs. Google’s custom silicon gives them a competitive advantage in developing innovative AI products.

Competitive Advantage Over GPUs

Ironwood provides a distinct advantage over traditional GPUs. Google’s custom TPU design delivers better efficiency and scale.

Google vs Nvidia, AWS, and Microsoft

Google has a decade-long lead in TPU deployment, compared to AWS and Microsoft. Nvidia’s GPUs remain popular but lack native TPU integration. Ironwood provides higher performance-per-watt for AI workloads. Google’s hardware and software integration ensures low-latency communication across large pods. 

Enterprises can achieve faster model training and smoother real-time inference. The chip’s scalability allows Google to outpace competitors in deploying large AI models globally. This competitive edge is reinforced by vertical integration across cloud and AI platforms.

Vertical Integration and Custom Silicon Strategy

Google designs both its hardware and software, optimizing every layer for AI workloads. This integration improves performance and efficiency compared to general-purpose GPUs. The TPU pods work seamlessly with Pathways software, enabling distributed training. Enterprises benefit from simplified deployment and reduced operational complexity. 

Custom silicon allows Google to control power and thermal efficiency, which is critical for scaling AI. This strategy ensures that large-scale AI models run reliably and efficiently. It also strengthens Google’s position as a leader in AI infrastructure.

Impact on Google Cloud and AI Revenue

Ironwood contributes directly to Google Cloud growth. Enterprises are investing in TPU-based AI solutions, boosting revenue.

Cloud Revenue Growth and Market Position

Google Cloud revenue grew 34% year-on-year, reaching $15.15 billion in Q3 2025. AI adoption is a key driver of this growth. Ironwood enables companies to train and deploy models faster and more efficiently. Its integration with Google Cloud ensures low-latency access to AI resources globally. 

Enterprises gain predictable performance for demanding workloads. Google’s leadership in custom AI hardware positions it strongly against Azure and AWS, attracting new customers and expanding market share.

Capital Expenditure and AI Infrastructure Expansion

To meet surging AI demand, Google raised its capital expenditure to $93 billion for 2025. This funding supports the expansion of data centers and TPU deployment. Ironwood pods require significant infrastructure investment but deliver high compute density and efficiency. 

The company is building scalable AI infrastructure capable of handling next-generation AI workloads. Google’s approach ensures AI models run reliably and cost-effectively. Expanding TPU capacity strengthens Google Cloud’s competitive advantage and future-proofs its AI offerings.

The Future of Inference: Powering Thinking AI Models

Ironwood is designed for the age of inference, where AI proactively generates insights. This marks a shift from reactive AI to generative intelligence.

Age of Inference Explained

The age of inference focuses on thinking AI models that anticipate user needs. Ironwood supports low-latency processing for these models, enabling faster insights. AI agents can retrieve, process, and generate information proactively, reducing human intervention. 

Large models like LLMs and MoEs benefit from massive parallel processing and high memory bandwidth. This allows organizations to deploy intelligent AI applications across industries, from finance to healthcare. The hardware-software integration ensures efficient scaling for increasingly complex workloads.

Enabling Advanced LLMs and AI Agents

Ironwood empowers advanced AI agents and large language models to perform complex reasoning. Its scalable TPU pods allow simultaneous training and inference for enormous models. Enterprises can deploy generative AI solutions at scale without bottlenecks. 

Pathways software ensures distributed computing is efficient and reliable. Ironwood also improves model responsiveness, delivering insights in real time. This combination of hardware and software sets a new benchmark for enterprise AI infrastructure, enabling innovation across sectors.

Frequently Asked Questions 

What Is Ironwood TPU?

Ironwood TPU is Google’s seventh-generation AI chip, designed for high-performance training and inference.

How Does Ironwood Compare to Nvidia GPUs?

Ironwood offers higher efficiency and better scalability for large AI models than traditional GPUs.

Who Is Using Ironwood?

Leading AI companies like Anthropic and several startups are deploying Ironwood for generative AI workloads.

When Will Ironwood Be Available on Google Cloud?

Ironwood will be widely available on Google Cloud in the coming weeks, after initial testing.

Why Is Ironwood Important for the Future of AI?

Ironwood enables thinking AI models, supports massive datasets, and sets a foundation for next-generation generative AI.

Conclusion

Ironwood represents a major leap in AI infrastructure, combining speed, efficiency, and scalability. Its deployment across Google Cloud empowers enterprises and startups to train massive AI models with reduced latency and energy costs. By integrating custom hardware with Google’s software stack, Ironwood sets a new standard for large-scale AI innovation. This chip positions Google as a leader in the AI ecosystem, ready for the age of inference.