Ironwood TPU: The Future of AI Inference and Intelligent Models
Introduction
Google’s most potent proprietary AI accelerator to date, Ironwood, the seventh-generation Tensor Processing Unit (TPU), was presented at Google Cloud Next ’25. Ironwood, designed specifically to power the next generation of inferential AI models, is a huge advancement in terms of performance as well as energy efficiency, memory bandwidth, and scalability.
Let’s take a closer look at Ironwood’s revolutionary characteristics, how it meets the changing needs of AI, and the practical applications it opens up for both developers and businesses.
The Evolution: Why New Chips Like Ironwood Were Needed
The field of AI is evolving quickly. These days, proactive AI that thinks, interprets, and produces insights on its own is more important than reactive AI models that react to cues. The “Age of Inference,” which is upon us, will require models to plan, reason, and make judgments without continual human input.
Large Language Models (LLMs), Mixture of Experts (MoEs),Model Context Protocol (MCP) and advanced scientific simulations are examples of next-generation “thinking models” that put previously unheard-of demands on technology.
- Massive parallel computation
- Ultra-low latency communication
- Extremely high memory bandwidth
- Energy-efficient performance at scale
Existing chip architectures were simply not enough.
That’s why Google built Ironwood, a TPU designed specifically for inference and reasoning at scale.
Introducing Ironwood: Google’s Most Powerful AI Accelerator Yet
Ironwood TPUs were made to smoothly handle the intricate communication and processing requirements of inferential AI models. It is about smarter, faster, and more efficient power, not just more power.
Key highlights of Ironwood:
- Built for the next generation of thinking AI models
- The chip’s processor size is 3 nanometers (3nm).
- Scales up to 9,216 liquid-cooled chips
- Supports 42.5 Exaflops per pod
- Features breakthrough Inter-Chip Interconnect (ICI) for ultra-fast communication
- Powers models beyond the limits of any single chip or server
Ironwood Chip Features: A Deep Dive
Here’s a closer look at what makes Ironwood the new gold standard for AI compute:
1. Extreme Compute Power
In order to handle the most dense LLMs and MoE models for both training and inference, each Ironwood chip has a peak compute of 4,614 TFLOPs.
2. Revolutionary Interconnect
Thousands of TPUs can communicate synchronously thanks to Ironwood’s Inter-Chip Interconnect (ICI), a low-latency, high-bandwidth network.
- Bandwidth: 1.2 TBps bidirectional (1.5x faster than previous generation)
- Reduced data movement boosts speed and efficiency at massive scale.
3. Expanded Memory
- 192 GB High Bandwidth Memory (HBM) per chip (6x Trillium’s capacity)
- Enables faster access to larger models and datasets
- HBM Bandwidth: 7.37 TB/s per chip (4.5x Trillium) — ideal for memory-intensive AI workloads.
4. Enhanced SparseCore
SparseCore accelerators allow Ironwood to efficiently handle ultra-large embeddings.
This is critical not just for AI, but also financial modeling, ranking systems, recommendation engines, and even scientific research.
5. Power Efficiency
- Ironwood offers 2x performance per watt,compared to Trillium, its predecessor.
- It’s nearly 30x more energy-efficient than the first Cloud TPU launched in 2018.
- Liquid cooling ensures sustained high performance even under heavy AI training loads.
- Pathways Software Stack
Google DeepMind created Pathways, which easily allows distributed computing among thousands of Ironwood chips.
It eliminates the difficulty of scaling AI models outside of a single pod.
Use Cases:
Google Cloud’s Ironwood TPU isn’t just about theoretical performance. It unlocks real-world applications across industries:
1. Super-Sized LLMs and Gen AI Models
Future iterations and models such as Google’s next-generation foundation model, Gemini 2.5, demand massive amounts of computing power. Ironwood offers the facilities needed to effectively train and service these models.
2. Scientific Breakthroughs
TPUs are already used in projects like AlphaFold, which solved the protein folding problem. The next generation of scientific breakthroughs in disciplines like astronomy, climate modeling, and medical research will be fueled by Ironwood’s memory and computing power.
3. Real-Time Decision-Making AI
Ironwood supports AI models that need to think and act in real-time with low latency, such as financial trading systems and autonomous driving.
4. Recommendation Engines and Ranking
Algorithms for ranking and suggestion are essential to search engines, streaming services, and e-commerce behemoths. The improvements made to Ironwood’s SparseCore guarantee ultra-large embedding processing, which produces faster and better recommendations.
5. Financial Modeling and Risk Analysis
Ironwood’s enormous memory capacity and low connection latency make it ideal for risk assessments, intricate simulations, and predictive modeling in the financial, banking, and insurance industries.
Configurations: Tailored to Your AI Needs
Google Cloud offers Ironwood TPUs in two configurations:
- 256 Chip Pod: Ideal for organizations getting started with high-performance AI inference and training.
- 9,216 Chip Pod: Delivers full 42.5 Exaflops, the world’s most powerful AI supercomputer configuration, 24x faster than the world’s leading traditional supercomputer (El Capitan).
Whether you’re training a cutting-edge AI model or deploying inference at scale, there’s an Ironwood configuration built for your mission.
Powering the Future: Why Ironwood Matters
Ironwood doesn’t just mark an evolutionary step; it represents a revolution in AI hardware.
Why Ironwood matters:
- Empowers AI agents that think, reason, and plan
- Accelerates the future of Gen AI, LLMs, and scientific discovery
- Solves the power consumption bottleneck with record-breaking perf/watt
- Optimized for sustainability, cost-efficiency, and scale
In a world where demand for AI compute is exploding, Ironwood provides an unmatched foundation for innovation today and for the decades to come.
Conclusion
Leading the way in AI infrastructure innovation is the Ironwood TPU.
Businesses, scientists, and AI developers may now address issues that were previously thought to be intractable since it is quicker, smarter, more scalable, and environmentally friendly.
Ironwood makes sure that Google Cloud users aren’t simply staying up, but are spearheading the emerging field of intelligent inference as AI quickly transitions from reactive to proactive.
Q&A
Q1. What is Ironwood TPU?
A. Ironwood is Google’s seventh-generation TPU built specifically for inferential AI models and thinking AI.
Q2. How powerful is Ironwood?
A. Each chip delivers 4,614 TFLOPs and a full 9,216-chip pod supports 42.5 Exaflops.
Q3. What is the Inter-Chip Interconnect (ICI)?
A. ICI is a low-latency, high-bandwidth network allowing thousands of TPUs to communicate synchronously.
Q4. How much memory does Ironwood have?
A. Each Ironwood chip features 192 GB of HBM with 7.37 TB/s memory bandwidth.
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