Marvell Technology shares surged nearly 6% on Monday following reports that Google may partner with the chip design firm on new artificial intelligence processors, while Broadcom shares fell करीब 2%.
According to reports, Google is considering Marvell for 2 upcoming chips designed to power AI workloads, potentially including a new Tensor Processing Unit (TPU) and a memory processing unit. Until now, Google has relied on Broadcom for TPU design, making this potential shift a notable development in its chip strategy. Neither Google nor Marvell immediately commented on the report.
Both Marvell and Broadcom play a key role in translating chip designs into silicon, offering backend support before manufacturing at large foundries like Taiwan Semiconductor Manufacturing Company. This segment has seen strong growth as tech companies increasingly build in-house AI accelerators.
Despite the reported move, Google’s partnership with Broadcom remains active. The companies recently extended their agreement through 2031 as part of a broader deal announced earlier this month.
The development comes as major tech players diversify chip sourcing to meet rising AI demand. Meta recently committed to deploying 1 gigawatt of its custom MTIA chips using Broadcom technology.
Marvell’s momentum has been strong, with its stock rising over 20% in March after solid quarterly results and continued to climb nearly 50% in April. Nvidia also announced a $2 billion investment in Marvell in March, aimed at expanding access to custom ASICs for its customers.
Google has been a pioneer in custom AI chips, launching its first TPU in 2015. Since then, companies like Amazon, Meta, Microsoft, and OpenAI have followed with their own silicon strategies to reduce reliance on Nvidia’s GPUs.
The company introduced its 7th-generation “Ironwood” TPU in November and is expected to unveil new chips at its upcoming Google Cloud Next conference. Originally built for internal use, Google’s TPUs have been available to cloud customers since 2018, with companies like Meta, Anthropic, and Apple now using them.
Meanwhile, supply constraints remain a challenge, especially in memory. Shortages from key suppliers such as Micron, SK Hynix, and Samsung continue to impact AI chip production.
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