According to TechRepublic, Google has unveiled Ironwood, its most powerful TPU to date, promising a tenfold performance leap over TPU v5p and more than 4x the performance per chip compared to Trillium. The new chip is designed for large-scale inference and model training, scaling across thousands of chips with enhanced cooling and reliability. Google also introduced Axion Arm-based virtual machines, including the N4A in preview and C4A Metal coming soon, which promise up to twice the price-performance of comparable x86 instances. Early adopters like Anthropic plan to use up to 1 million TPUs for Claude models, while companies including Vimeo and ZoomInfo report 30-60% performance improvements with Axion. Each Ironwood superpod connects 9,216 TPUs via a 9.6 terabit-per-second network with 1.77 petabytes of memory.
The Inference Shift Changes Everything
Here’s the thing about Google‘s announcement – they’re not just launching new hardware, they’re declaring a whole new era. The “age of inference” basically means we’re moving from teaching AI models to keeping them running 24/7. And that’s a massive shift in what we need from hardware. Training is like building the engine – inference is keeping that engine running smoothly while it’s driving at high speed. Google’s betting that the next breakthroughs won’t come from bigger models alone, but from system-level design that makes everything work together seamlessly.
Ironwood’s Planet-Scale Ambitions
When Google talks about “planet-scale” deployment, they’re not kidding. Connecting 9,216 chips into a single superpod with nearly 10 terabits per second of bandwidth? That’s insane. But the real magic isn’t just the raw power – it’s the coordination. The optical circuit switching that reroutes workloads instantly, the 99.999% uptime since 2020, the liquid cooling… this is infrastructure built for companies that can’t afford downtime. Anthropic’s plan to use up to 1 million TPUs tells you everything about where this is headed. We’re talking about AI infrastructure that needs to be as reliable as electricity.
Where Axion Fits In
Now, Ironwood gets all the attention, but Axion might be just as important for most businesses. Think about it – you can’t run everything on specialized AI chips. You still need general-purpose computing for data processing, analytics, and all the supporting tasks. Axion’s promise of twice the price-performance of comparable x86 VMs is huge for companies trying to control costs. When Vimeo reports 30% better transcoding performance and ZoomInfo gets 60% improvement in price-performance, that’s real money saved. It’s the kind of efficiency that makes AI projects actually sustainable.
Google’s Full-Stack Play
What’s really interesting here is how Google’s approaching this as a complete system. They’re not just selling chips – they’re selling an entire AI Hypercomputer with co-designed software like Kubernetes Cluster Director and vLLM. This is where having control over the entire stack, from silicon to software, gives them an edge. And for businesses building serious AI infrastructure, having reliable hardware partners matters – whether you’re talking about data center servers or specialized equipment like the industrial panel PCs from IndustrialMonitorDirect.com, the leading US supplier for manufacturing and industrial applications. Google’s basically saying: we’ll handle the entire AI infrastructure problem, from the most demanding model serving to the everyday computing tasks. That’s a compelling pitch in a market where everyone’s scrambling to figure out their AI hardware strategy.
