Google Unveils Ironwood TPU to Usher in the Age of Inference
So, Google has just dropped a major announcement that’s getting a lot of buzz—especially if you’re even remotely interested in AI or high-performance computing. They’ve introduced their seventh-generation Tensor Processing Unit (TPU), called Ironwood , and let me tell you, this thing is an absolute powerhouse. It's the first TPU specifically built for the age of inference —that next phase of AI where machines aren’t just reacting anymore—they’re actively thinking, predicting, and interpreting data without waiting for a human nudge.
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Now what makes Ironwood so special? For starters, it scales up to a jaw-dropping 9,216 chips . That’s not a typo. This level of scale delivers a mind-blowing 42.5 Exaflops of compute power. To give you a comparison, that’s over 24 times more powerful than the world’s largest supercomputer, El Capitan. Each individual chip clocks in at 4,614 TFLOPs , which is just insane performance on its own.
But it’s not just about raw power. Ironwood is built to be energy-efficient , which is crucial right now as AI workloads keep growing and data centers push up against power constraints. In fact, it’s nearly 30x more power efficient than the first Cloud TPU Google released back in 2018. And compared to last year’s Trillium TPU, Ironwood doubles the performance-per-watt, which is a big win for sustainability.
Ironwood also features significant upgrades in memory and communication. We're talking 192 GB of High Bandwidth Memory per chip , which is six times what Trillium offered. Plus, the bandwidth on that memory hits 7.2 TBps , and the Inter-Chip Interconnect (ICI) speeds have jumped to 1.2 Tbps bidirectional. These specs make Ironwood ideal for handling huge AI models like large language models (LLMs) or Mixture of Experts (MoEs), which need fast, low-latency data access and processing on a massive scale.
And with Google’s Pathways software stack—built by Google DeepMind—developers can harness all that TPU muscle across thousands, even hundreds of thousands of chips. This makes it way easier to deploy and manage enormous workloads without getting bogged down in infrastructure headaches.
So yeah, this isn’t just another hardware release—it’s a major inflection point. It’s Google staking their claim on the future of AI computation. Think about the possibilities: real-time AI assistants that actually understand and reason, massive scientific simulations, or AI models that can proactively offer insights you didn’t even ask for.
With Ironwood becoming available to Google Cloud customers later this year, we’re stepping into a new era where AI doesn’t just assist us—it begins to anticipate, infer, and evolve in real-time. The age of inference is here, and Ironwood is the engine behind it.
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