公布技术参数“颗粒度” 大厂接连“秀肌肉” 自研AI芯片为何不再“闷声干”?

Core Viewpoint - The recent announcements from major Chinese tech companies like Huawei and Baidu regarding their AI chip development signify a shift in the domestic semiconductor landscape, aiming to fill the gap left by Nvidia and enhance the competitiveness of Chinese AI chips [3][4][5]. Group 1: AI Chip Development - Major Chinese companies are increasingly revealing their AI chip roadmaps, with Huawei planning to release four Ascend AI chips over the next three years, while Baidu is set to launch two Kunlun AI chips in the next two years [2][3]. - Huawei's detailed disclosure of technical parameters for its chips, including bandwidth, computing power, and memory, marks a significant change in the traditionally low-profile approach of Chinese chipmakers [2][7]. - The introduction of supernodes and clusters is seen as a critical strategy for overcoming the limitations of China's semiconductor manufacturing processes, which are currently capped at the 7nm node [10][12]. Group 2: Competitive Landscape - The competition in the global AI chip market is characterized as asymmetric, with Chinese chips lagging behind North American counterparts like Nvidia in various technical specifications, yet capable of leveraging networking capabilities to surpass them in performance [5][6]. - Huawei's Ascend series has been recognized as a formidable competitor, with its first chip released in 2018 and subsequent iterations showing significant performance improvements despite challenges posed by U.S. sanctions [6][8]. - Baidu's Kunlun chip, while still behind in performance compared to Nvidia's offerings, is focusing on cost-effectiveness and specific use cases, indicating a strategic approach to market entry [8][9]. Group 3: Market Dynamics - The domestic AI chip market is witnessing a shift towards inference tasks, with inference scenarios accounting for 42% of the GenAI IaaS service market, while training scenarios have decreased to 58% [14][15]. - The challenges of using domestic AI chips for large model training are acknowledged, with companies like Huawei and Baidu working to adapt their technologies to meet these demands [14][15]. - The push for self-developed chips by major cloud providers is seen as a way to reduce costs and improve performance, with companies like Kunlun seeking to penetrate external markets [16][17].