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AMD buys ZT for $4.9 billion to boost AI GPU capabilities

AMD has a string of AI-related investments; this one focuses on hyperscaler AI compute

In its continued pursuit of a stronger arsenal of artificial intelligence capabilities, chip company AMD announced another major acquisition: A $4.9 billion cash-and-stock agreement to acquire New Jersey-based ZT Systems, which provides AI and general-purpose compute for hyperscalers.

“The strategic transaction marks the next major step in AMD’s AI strategy to deliver leadership AI training and inferencing solutions based on innovating across silicon, software and systems,” AMD said in a release. “ZT Systems’ extensive experience designing and optimizing cloud computing solutions will also help cloud and enterprise customers significantly accelerate the deployment of AMD-powered AI infrastructure at scale.”

AMD said that after closing—which is expected in the first half of 2025—it will look for a buyer to acquire ZT Systems’ U.S.-based data center infrastructure manufacturing business. Published press reports speculated that the 1,000 cloud data engineers that ZT Systems brings to AMD are at the core of the value that AMD saw in the business.

“Our acquisition of ZT Systems is the next major step in our long-term AI strategy to deliver leadership training and inferencing solutions that can be rapidly deployed at scale across cloud and enterprise customers,” said AMD Chair and CEO Dr. Lisa Su. “ZT adds world-class systems design and rack-scale solutions expertise that will significantly strengthen our data center AI systems and customer enablement capabilities. This acquisition also builds on the investments we have made to accelerate our AI hardware and software roadmaps. Combining our high-performance Instinct AI accelerator, EPYC CPU, and networking product portfolios with ZT Systems’ industry-leading data center systems expertise will enable AMD to deliver end-to-end data center AI infrastructure at scale with our ecosystem of OEM and ODM partners.”

A new report from Futurum Intelligence estimates that the GPUs accounted for 74% of chipsets used in AI applications within data centers; it expects the GPU market in data centers to see a compound annual growth rate of 30% over the next five years, with the market jumping from $28 billion in 2023 to $102 billion by 2028. Futurum said that Nvidia held 92% of the market share for GPUs in early 2024. 

In July of this year, AMD announced that it is buying the largest private artificial intelligence lab in Europe, Silo AI, for $665 million in cash, to bolster AI solutions for enterprise that will leverage AMD’s chips; that transaction closed earlier this month. The move was widely seen as an effort by AMD to not only better enable its customers to build AI models based on AMD chip platforms, but to help the company compete better against Nvidia.

“AI is our number one strategic priority, and we continue to invest in both the talent and software capabilities to support our growing customer deployments and roadmaps,” said Vamsi Boppana, AMD senior vice president, AIG, upon closing of the Silo AI transaction.

AMD has been on an AI buying spree, purchasing Mipology and Nod.ai and investing more than $125 million in a dozen other AI companies in the past 12 months. The company said that, excluding the ZT Systems transaction, it has invested more than $1 billion to expand the AMD AI ecosystem and strengthen its AI software capabilities.

Looking back beyond 12 months, the company spent tens of billions in the last couple of years to buy Xilinx and Pensando Systems.

ABOUT AUTHOR

Kelly Hill
Kelly Hill
Kelly reports on network test and measurement, as well as the use of big data and analytics. She first covered the wireless industry for RCR Wireless News in 2005, focusing on carriers and mobile virtual network operators, then took a few years’ hiatus and returned to RCR Wireless News to write about heterogeneous networks and network infrastructure. Kelly is an Ohio native with a masters degree in journalism from the University of California, Berkeley, where she focused on science writing and multimedia. She has written for the San Francisco Chronicle, The Oregonian and The Canton Repository. Follow her on Twitter: @khillrcr