Qualcomm to launch data center processors that link to Nvidia chips
Qualcomm on Monday said it plans to launch processors designed for data centers to power artificial intelligence, which will link to Nvidia's chips.
Nvidia's graphics processing units (GPUs) have become a critical component in data centers used to train huge AI models that power applications like chatbots. They are often paired with a central processing unit (CPU) — a market dominated by Intel and AMD.
Qualcomm said it is planning to launch a custom CPU for the data center that can connect to Nvidia's GPUs and software. A link to Nvidia's infrastructure is key for any player hoping to crack into the data center given the relevance of the U.S. chip giant's semiconductors for AI.
The announcement actually marks a re-entry into the data center market for Qualcomm after previous efforts last decade bore little fruit.
In 2021, Qualcomm acquired Nuvia, which designs processors based on Arm designs and has been key to the U.S. tech titan's efforts with data center CPUs.
The data center CPU market remains highly competitive. Big cloud computing players like Amazon and Microsoft already design and deploy their own custom CPUs. AMD and Intel also have a strong presence.
Last week, Qualcomm signed a memorandum of understanding with Saudi-based AI frim Humain to develop data centers, joining a slew of U.S. tech companies making deals in the region. Humain will operate under Saudi Arabia's Public Investment Fund.
Getting into the data center is part of a broader strategy from Qualcomm to diversify its business, which has traditionally been dominated by sales of processors and modems for smartphones.
Under CEO Cristiano Amon, Qualcomm has moved into chips for cars as well as PCs — a market historically dominated by Intel.
Amon said on Monday that there are now more than 85 PC designs that are on sale or in development with Snapdragon X Series chips, which were first announced in 2023.
Qualcomm is pitching its chips as power efficient and capable of running AI processes on a device, rather than in the cloud, where they are typically done now. Some of the touted advantages of on-device AI include faster AI apps and more security, since data doesn't need to leave a piece of hardware.
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