Google Unveils Ironwood: A Giant Leap in AI Inference Power. Google has officially introduced Ironwood, its seventh-generation Tensor Processing Unit (TPU), setting a new standard in AI hardware performance. Announced at Google Cloud Next ’25, Ironwood is not only the company’s most powerful and scalable custom AI accelerator to date—it is also the first designed specifically for inference, marking a major milestone in the evolution of artificial intelligence.
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Previous TPU generations focused primarily on training AI models—teaching them how to understand and process data. Ironwood changes the game by being purpose-built for inference—the phase where trained models are actually used in real-world applications. As AI moves from research laboratories to commonplace tools, goods, and experiences, this change is essential. With Ironwood, Google is getting ready for the “age of inference,” when AI models will actively retrieve, analyse, and produce data to provide insightful analysis rather than only reacting to commands.
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Ironwood’s raw capabilities are truly groundbreaking. A single Ironwood chip offers 4,614 TFLOPs of compute, and when scaled up to a full pod of 9,216 chips, it delivers an astonishing 42.5 exaflops of compute power. For context, that’s over 24 times faster than El Capitan, the world’s most powerful supercomputer.
42.5 Exaflops per full pod
192 GB of High Bandwidth Memory (HBM) per chip – 6x more than its predecessor
7.2 TB/s HBM bandwidth per chip – 4.5x improvement
1.2 Tbps bidirectional Inter-Chip Interconnect (ICI) bandwidth
2x perf/watt efficiency compared to previous TPU generation (Trillium)
30x power efficiency compared to Google’s first-generation TPU
And it’s not just fast—it’s smart. The enhanced SparseCore in Ironwood is a specialised accelerator designed to process ultra-large embeddings, which are utilised in scientific workloads, financial modelling, and recommendation systems.
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Ironwood is built to handle the growing computational demands of cutting-edge AI models such as:
Large Language Models (LLMs)
Mixture of Experts (MoEs)
Advanced reasoning models like Gemini 2.5 and AlphaFold
These models require massive parallel processing, minimal latency, and high memory bandwidth. Ironwood addresses these needs with a liquid-cooled architecture and an advanced ICI network that links thousands of chips seamlessly.
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To help developers harness this hardware at scale, Google is leveraging Pathways, its proprietary machine learning runtime. Developed by Google DeepMind, Pathways enables seamless distributed computing across thousands of TPUs. With less complexity and more efficiency, developers can easily grow AI applications thanks to this software architecture.
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In the era of increasing energy concerns, Ironwood also prioritizes sustainability. It can retain peak performance while using the least amount of electricity because to its sophisticated processor architecture and liquid cooling. This effectiveness lowers expenses while also assisting businesses in scaling AI applications responsibly.
30x more power-efficient than the original TPU from 2018
Delivers more capacity per watt than any previous generation
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Ironwood reflects Google’s deeper commitment to pushing AI beyond theory and into real-life applications. Every day, billions of interactions across Google Search, Gmail, and other platforms are already powered by the TPU. The business is now focussing twice as hard on enterprise-grade AI performance, efficiency, and scalability with Ironwood.
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Ironwood isn’t just a chip—it’s a statement. A statement that the future of AI lies not only in training bigger models but in using them faster, more intelligently, and more sustainably in the real world. With the release of Ironwood, Google is paving the way for a new wave of inference-driven innovations and applications.
From research to reality—the age of inference is here.
