Chinese AI firms mixing different GPUs inside individual AI servers to combat GPU shortages from US sanctions

AI data center
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DigiTimes Asia reports that Chinese AI firms are implementing a multi-chip hybrid approach to improve AI capabilities. The multi-chip approach also brings several advantages. These include increasing LLM training speeds using multi-GPU parallel training, allowing more data to be processed simultaneously for better memory utilization, and reducing costs by not relying solely on expensive Nvidia chips.

Chinese tech companies are bundling GPUs from different suppliers for their AI training needs to circumvent American sanctions limiting their access to advanced hardware. With the White House taking active measures to stop U.S.-made tech from entering China, like revoking eight of Huawei's export licenses in 2024, data center GPUs required for advanced AI processing are getting more complicated in the East Asian Country.

To make this possible, Chinese firms have started developing 'multi-chip hybrid' technologies that would allow them to combine different chips into a single training cluster. For example, Baidu announced during its 2024 earnings call that it could combine GPUs from various vendors and use them for AI training. Another major Chinese tech company, Alibaba, has worked on a 'one cloud, multiple chips' solution since 2021.

Using different GPUs on a single AI server has challenges, like needing a high-speed fabric like Nvidia's NVLink to ensure disparate accelerators can communicate efficiently. However, Chinese tech companies are also pushing innovation that way, with Alibaba Cloud ditching it for its ethernet-based High-Performance Network.

Jowi Morales
Contributing Writer

Jowi Morales is a tech enthusiast with years of experience working in the industry. He’s been writing with several tech publications since 2021, where he’s been interested in tech hardware and consumer electronics.