TPU

Plain English. A TPU (tensor processing unit) is Google's own AI accelerator, designed in-house rather than bought from Nvidia. Purpose-built for the tensor maths that models run on, TPUs power Google's own training and serving and are rented out through Google Cloud. They are the most mature example of a hyperscaler making its own silicon.

Why it moves money. The TPU is vertical integration as a cost weapon: by designing the chip, the software and the data centre together, Google sidesteps Nvidia's margin and controls its own supply. Independent testing has started to back the pitch — Google's latest Ironwood generation was measured at markedly better performance per dollar than Nvidia's top parts — which turns the TPU from a captive tool into a competitive threat if Google chooses to sell capacity aggressively. It is the in-house counterpart to the merchant GPU and fixed-function ASICs, and a live case study in custom silicon.

What to watch. Whether TPUs win serious workloads outside Google's own fleet, and whether the per-dollar advantage holds in third-party tests rather than vendor claims. External adoption is what would reprice the whole accelerator market.

From the signals. SemiAnalysis measured the Ironwood TPU at up to 50% better per dollar than Nvidia's B200/B300. Google took a share-purchase right in Marvell tied to chip volumes out to 2033.