The Ironwood processor is geared toward the type of data crunching needed when users query software such as OpenAI's ChatGPT. Known in the tech industry as "inference" computing, the chips perform rapid calculations to render answers in a chatbot or generate other types of responses.
The search giant's multi-billion dollar, roughly decade-long effort represents one of the few viable alternative chips to Nvidia's (NVDA.O) powerful AI processors.
Google's tensor processing units (TPUs) can only be used by the company's own engineers or through its cloud service and have given its internal AI effort an edge over some rivals.
For at least one generation Google split its TPU family of chips into a version that's tuned for building large AI models from scratch. Its engineers have made a second line of chips that strips out some of the model building features in favor of a chip that shaves costs of running AI applications.
The Ironwood chip is a model designed for running AI applications, or inference, and is designed to work in groups of as many as 9,216 chips, said Amin Vahdat, a Google vice president.
The new chip, unveiled at a cloud conference, brings functions from earlier split designs together and increases the available memory, which makes it better suited for serving AI applications.
"It's just that the relative importance of inference is going up significantly," Vahdat said.
The Ironwood chips boast double the performance for the amount of energy needed compared with Google's Trillium chip it announced last year, Vahdat said. The company builds and deploys its Gemini AI models with its own chips.
The company did not disclose which chip manufacturer is producing the Google design.
(This story has been corrected to fix the name of the Google executive to Amin Vahdat, not Amind Vahdat, in paragraph 6)
