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April 19 (Reuters) - Alphabet's Google (GOOGL.O), is in talks with Marvell Technology (MRVL.O), to develop two new chips aimed at running AI models more efficiently, The Information reported on Sunday citing two people with knowledge of the discussions.
One of the chips is a memory processing unit designed to work with Google's tensor processing unit (TPU) and the other chip is a new TPU built specifically for running AI models, the report said.
Google has been pushing to make its TPUs a viable alternative to Nvidia's dominant GPUs. TPU sales have become a key driver of growth in Google's cloud revenue as it aims to show investors that its AI investments are generating returns.
Reuters could not immediately verify the report. Google and Marvell did not immediately respond to a request for a comment.
The companies aim to finalize the design of the memory processing unit as soon as next year before handing it off for test production, according to the report.
Google has been using ASIC providers since day one.
Two questions: Why don't they just do it themselves? Why would Google switch providers after working with AVGO for so many years?
IP was a big reason why companies chose ASIC providers. Avago used to have the best Serdes so Google worked with them for many years. Price is also a big reason for changing ASIC providers but it has to be a big difference since trust is also a big thing.
Maybe Google is looking for supply chain resilience? Google could also be using Marvell as a pricing leverage for Broadcom? The ASIC business is a difficult one.
Thousands of CEOs admit AI had no impact on employment or productivity—and it has economists resurrecting a paradox from 40 years ago
In 1987, economist and Nobel laureate Robert Solow made a stark observation about the stalling evolution of the Information Age: Following the advent of transistors, microprocessors, integrated circuits, and memory chips of the 1960s, economists and companies expected these new technologies to disrupt workplaces and result in a surge of productivity. Instead, productivity growth slowed, dropping from 2.9% from 1948 to 1973, to 1.1% after 1973....
We found that industries in states that were more exposed to AI experienced faster productivity growth beginning in 2021 – before ChatGPT reached the public – driven by enterprise tools already embedded in professional workflows, including GitHub Copilot for software development, Jasper for marketing and content writing, and Microsoft’s GPT-3-powered business applications. In 2024, for example, industries whose AI exposure was one standard deviation higher saw gains of 10% in productivity, 3.9% in jobs and 4.8% in wages than comparable industries in the same state.
