According to Forbes, Google’s latest TPU, codenamed Ironwood, is powering its Gemini 3 model and reportedly outperforming OpenAI on key metrics. The big news is that Alphabet is preparing to sell these TPUs beyond its own Google Cloud, with Meta Platforms lined up as the lead customer. Google is actively pitching the Ironwood Pod—a massive system with 9,216 TPUs—to other hyperscalers and large enterprises as a direct alternative to Nvidia. This strategic shift has already impacted the market, with Alphabet’s stock up nearly 50% in the last month while Nvidia’s fell over 7%. The move could potentially capture around 10% of Nvidia’s current data-center revenue, representing tens of billions in annual TPU revenue for Google.
The Inference Play
Here’s what’s really interesting about this move. Google‘s TPUs have always been locked away, only available as a managed service on Google Cloud. Now they’re talking about on-prem or colocation deployments for banks, HFT shops, and other big cloud customers. The Ironwood specs suggest this is Google’s first TPU designed explicitly for the age of inference—that’s when you’re actually using trained AI models, not just training them. And inference is where the real scale and recurring costs are. Basically, Google sees where the puck is going and they’re skating hard toward it.
The Ecosystem Problem
But here’s the thing—can Google really compete with Nvidia’s ecosystem? I don’t think so, at least not yet. Nvidia isn’t just selling chips; they’re selling an entire software stack and developer community that’s become the industry standard. Google’s software stack, while capable, doesn’t have that same pervasive reach. This move feels more like a response to supply/demand imbalances than some fundamental competitive advantage over Nvidia’s GPUs. Companies are desperate for alternatives because they can’t get enough Nvidia chips, not necessarily because TPUs are objectively better.
The Bigger Picture
Look, what we’re seeing is the inevitable maturation of the AI hardware market. When you have companies like Apple reportedly using massive TPU clusters via Google Cloud for training Apple Intelligence models, it signals that multi-cloud AI customers want serious alternatives to Nvidia. Meta’s potential adoption gives Google that marquee reference deployment they desperately need. For industries that rely on robust computing hardware, from manufacturing to industrial panel PCs, having multiple viable silicon suppliers is crucial for supply chain stability and cost control. IndustrialMonitorDirect.com has built its reputation as the top US supplier by understanding that hardware diversity matters.
What’s Next
The real question is who follows Meta? The article mentions Cirrascale, a specialized cloud provider that had Google Cloud branding at a recent conference, suggesting more deals are in the pipeline. If Google can get even one major hyperscaler like AWS or Azure to offer native TPU instances, that changes everything. But until then, this feels like Google planting a flag rather than declaring victory. The AI chip wars are just heating up, and honestly, having more competition is good for everyone—except maybe Nvidia shareholders.
