Chinese tech firm Meituan launched a new artificial intelligence model on Tuesday that it said was the first of its size to be trained using domestically developed computer chips. <\/p>\n
The country is locked in a fierce race for AI dominance with the United States, which restricts the export to China of the most cutting-edge silicon chips designed by industry leader Nvidia. <\/p>\n
In response to Washington’s restrictions, imposed on national security grounds, China has been accelerating efforts to develop its own advanced chips and break away from reliance on US hardware. <\/p>\n
Meituan on Tuesday unveiled LongCat-2.0, a new large language model whose performance it says is comparable to Google’s Gemini 3.1 pro, released in February. <\/p>\n
LLMs are the kind of technology that underpin chatbots and many other AI tools. <\/p>\n
LongCat-2.0 is “the industry’s first trillion-parameter model to complete end-to-end training and inference on a 50,000-chip domestic compute cluster”, Meituan said in a statement. <\/p>\n
It did not disclose which Chinese chipmaker’s products were used in the training process. <\/p>\n
The announcement marks a milestone for China’s fast-developing AI industry, because training competitive models with vast amounts of digital data requires chips packing hefty computing power. <\/p>\n
Flagship models from other Chinese AI labs, including DeepSeek and Zhipu, can run inference on chips produced by domestic companies like Huawei — a less intensive process than training. <\/p>\n
It is believed that most domestic models were trained using Nvidia chips. Some media reports have suggested that an unknown number of advanced Nvidia AI chips were smuggled into China. <\/p>\n
Meituan’s AI research team began exploring the use of domestic chips in 2023 and “has proven that we are now capable of carrying out large-scale model training on domestic compute clusters”, the company said.<\/p>\n","protected":false},"excerpt":{"rendered":"
Chinese tech firm Meituan launched a new artificial intelligence model on Tuesday that it said was the first of its size to be trained using domestically developed computer chips. The country is locked in a fierce race for AI dominance with the United States, which restricts the export to China of the most cutting-edge silicon chips designed by industry leader Nvidia. In response to Washington’s restrictions, imposed on national security grounds, China has been accelerating efforts to develop its own advanced chips and break away from reliance on US hardware. Meituan on Tuesday unveiled LongCat-2.0, a new large language model […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1017864","post","type-post","status-publish","format-standard","hentry","category-it-2"],"acf":[],"_links":{"self":[{"href":"https:\/\/telecomlive.in\/web\/wp-json\/wp\/v2\/posts\/1017864","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/telecomlive.in\/web\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/telecomlive.in\/web\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/telecomlive.in\/web\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/telecomlive.in\/web\/wp-json\/wp\/v2\/comments?post=1017864"}],"version-history":[{"count":1,"href":"https:\/\/telecomlive.in\/web\/wp-json\/wp\/v2\/posts\/1017864\/revisions"}],"predecessor-version":[{"id":1017885,"href":"https:\/\/telecomlive.in\/web\/wp-json\/wp\/v2\/posts\/1017864\/revisions\/1017885"}],"wp:attachment":[{"href":"https:\/\/telecomlive.in\/web\/wp-json\/wp\/v2\/media?parent=1017864"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/telecomlive.in\/web\/wp-json\/wp\/v2\/categories?post=1017864"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/telecomlive.in\/web\/wp-json\/wp\/v2\/tags?post=1017864"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}