TPUs: Google's home advantage

"So one of the main advantages of TPUs for Google is that it designs them, it has them manufactured through TSMC, which means it has sole dibs on them. This avoids a lot of the supply chain bottlenecks we've seen in recent years where companies have been queuing up for Nvidia chips. Nation states have been queuing up for Nvidia chips, or they've been forced to buy them in massive quantities up front and then slowly figure out where they're going to deploy them all."

"Nvidia really is setting the standard on enterprise AI hardware. Like you said, Intel, AMD are also major players in the space and they have their chunk of the market, but Nvidia is the one that the major AI developers come back to again and again. It's making the hundred billion dollar announcements, all of this investment that seems to be announced every month."

Companies have invested heavily in GPUs to meet the parallel processing needs of generative AI models, with Nvidia projecting massive sales driven by inference demand. Inference now demands real-time reasoning at planetary scale, increasing compute intensity. Google relies on in-house tensor processing units (TPUs) for both training and inference, designing and contracting their manufacture to TSMC to secure supply. TPUs can avoid external chip shortages and be tailored for specific AI workloads, potentially giving Google cost and performance benefits. Trade-offs include potential latency penalties, reduced flexibility across workloads, and divergence from the broader GPU-driven ecosystem.

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