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Home Metaverse

Baidu Unveils New Tech to Simplify AI Model Training – Metaverseplanet.net

25/04/2025
in Metaverse
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Baidu Unveils New Tech to Simplify AI Model Training – Metaverseplanet.net


Chinese language expertise big Baidu has introduced a groundbreaking new resolution designed to make coaching AI fashions a lot simpler. The corporate launched a large chip cluster constructed from its self-developed third-generation P800 Kunlun chips, able to coaching advanced fashions like DeepSeek.

In keeping with Baidu CEO Robin Li, the newly created chip cluster consists of 30,000 P800 Kunlun chips. This monumental system is highly effective sufficient to coach extremely advanced AI fashions containing tons of of billions of parameters. Furthermore, hundreds of customers engaged on smaller fashions will be capable of entry the system concurrently to develop their very own tasks.

Baidu’s Chips Are Already in Use

Baidu Unveils New Tech to Simplify AI Model Training

Baidu has revealed that its newly developed chips are already being utilized by banks and web corporations throughout China. Along with the chip announcement, Baidu launched two new AI fashions: Ernie 4.5 Turbo and Ernie X1 Turbo. These fashions are designed for quite a lot of functions, together with language processing and programming, and shall be accessible to customers by way of Baidu’s varied platforms.

China’s AI Race Intensifies

FILE PHOTO: Males work together with a Baidu AI robotic close to the corporate emblem at its headquarters in Beijing, China April 23, 2021. REUTERS/Florence Lo

Just lately, the synthetic intelligence race in China has accelerated, with corporations now focusing not simply on creating AI fashions but in addition on integrating them into sensible functions.

Among the many standout gamers on this new wave is Baidu, and its newest technological leap clearly alerts its ambition to strengthen its AI investments by way of real-world functions. This transfer highlights Baidu’s dedication to bridging the hole between AI improvement and sensible deployment.

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