Chinese chip design company Moore Thread Technology has launched a series of new products, including next-generation chip architecture and artificial intelligence chips, in a bid to challenge US chip manufacturing giants Nvidia and AMD.
The Beijing-based company, dubbed China’s “Little Nvidia,” unveiled two of its upcoming chips, “Huashan” and “Lushan,” named after two famous Chinese mountains, at a developer conference in the Chinese capital on Saturday.
Moore Threads founder, chairman and CEO Zhang Jianzhong said that the Huashan chip is specially designed for artificial intelligence training and inference, and its performance is better than Nvidia’s Hopper series and close to the latest Blackwell series of American technology companies.
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The launch marks Moore Threads’ latest move to challenge foreign rivals such as Nvidia, and comes on the heels of the company’s blockbuster initial public offering on the Shanghai Stock Exchange on Dec. 5.
Moore Thread founder and CEO Zhang Jianzhong, photographed in November 2023. Image: Handout alt=Jianzhong Zhang, founder and CEO of Moore Thread, taken in November 2023. Picture: Handout>
The company’s share price has risen more than 480% since its listing, closing at 664.1 yuan on Friday.
Zhang claimed that Huashan’s performance surpassed Nvidia’s Hopper series (including H100 and H200 chips) in terms of computing power, memory bandwidth and capacity, but did not provide specifications for the chips.
Until recently, the U.S. government banned the sale of Nvidia’s H100 and H200 (a popular choice among global tech companies for artificial intelligence training) to China, citing national security concerns.
Washington recently unexpectedly approved the sale of H200 chips to China, balancing export controls, economic interests and national security concerns. But Beijing has not yet approved its domestic sales.
Zhang, 59, is an Nvidia veteran and former China market general manager who left in 2020 to found Moore Threads. He acknowledged that local AI developers had previously been hesitant to use domestic chips to train their AI models, fearing poor results.
“But now we can confidently say that if you’ve used Hopper before [chips]switching to our new product for your large language models will yield better results,” he said.
