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Home - Asia - China’s First 30,000-Card Nationwide Supercomputing Web Core Node Enters Trial Operation
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China’s First 30,000-Card Nationwide Supercomputing Web Core Node Enters Trial Operation

NextTechBy NextTechFebruary 6, 2026No Comments2 Mins Read
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China’s First 30,000-Card Nationwide Supercomputing Web Core Node Enters Trial Operation
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In keeping with Sugon, on February 5, the Nationwide Supercomputing Web Software Expertise Convention and Core Node Trial Operation Launch Ceremony was held in Zhengzhou, Henan Province.

Three 10,000-card supercluster methods (scaleX 10,000-Card Tremendous Cluster), offered by Sugon, concurrently entered trial operation on the Zhengzhou core node of the Nationwide Supercomputing Web. This marks the nation’s first operational deployment of a 30,000-card AI computing cluster, making it the biggest domestically developed AI computing pool in precise operation so far. The cluster totally helps large-scale AI workloads, together with trillion-parameter mannequin coaching, high-throughput inference, and AI for Science functions.

In 2023, China’s Ministry of Science and Expertise convened a devoted assembly to coordinate and launch the event of the Supercomputing Web. In April 2023, development of the Nationwide Supercomputing Web Platform formally started, with the institution of the Nationwide Supercomputing Web Consortium. The Nationwide Excessive Efficiency Computing Engineering Expertise Analysis Middle serves because the consortium’s chair unit.

On January 4, the Nationwide Supercomputing Web Platform introduced that its consumer base had formally exceeded a million customers. The platform has now linked 14 provinces and municipalities, integrating greater than 30 nationwide supercomputing and clever computing facilities. Collectively, they kind the nation’s largest heterogeneous computing useful resource pool, with over 150,000 accelerator playing cards and greater than 2 million CPU cores, providing customers almost 70 totally different computing configurations.

Supply: IThome

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