Google officially launched its Ironwood tensor processing unit in early November. A TPU, or tensor processing unit, is an application-specific integrated circuit optimized for the kinds of math deep-learning models use. Last week, The Information reported that Meta is in talks to buy billions of dollars’ worth of Google’s Artificial Intelligence chips starting in 2027, a development that sent Nvidia’s stock sliding as investors weighed a credible alternative to the GPU-driven status quo.
Ironwood’s debut reflects a broader shift in workloads from massive, capital-intensive training runs to cost-sensitive, high-volume inference tasks that underpin chatbots and agentic systems. Ironwood now powers Google’s Gemini 3 model and the company says the chip is designed for responsiveness and efficiency rather than brute-force training. The TPU ecosystem is expanding: Samsung and SK Hynix are reportedly taking production and packaging roles, and Anthropic intends to access up to one million TPUs from Google Cloud in 2026 as part of a diversified compute strategy alongside Amazon’s Trainium custom ASICs and Nvidia GPUs.
The market implications are significant but incremental. Nvidia still controls more than 90 percent of the Artificial Intelligence chip market and continues to supply Google with Blackwell Ultra GPUs such as the GB300. Analysts characterize the moment as Google’s Artificial Intelligence comeback and say hyperscalers and semiconductor rivals – including AMD, which is winning ground on inference workloads – can chip away at Nvidia’s leadership over time. Google is not likely to displace Nvidia overnight, but Ironwood and a growing TPU-Gemini stack force the industry to consider a more pluralistic future for Artificial Intelligence infrastructure.
