直击WAIC 2025｜无问芯穹CEO夏立雪：算力紧缺根源在“供需错配”，要让国产算力即插即用、像超市商品般可自由挑选

Core Viewpoint - The demand for AI computing power is surging, leading to a focus on the diversification and localization of computing resources in the industry [1] Group 1: AI Computing Power Landscape - The current domestic chip and computing resource landscape is diverse, with multiple independent ecosystems, but significant differences in hardware architecture and interface protocols hinder the efficiency of AI technology implementation [1] - The company, Wunwen Xinqiong, has developed a "universal language" for the industry that enables seamless communication and collaboration between different chips, allowing developers to avoid the complexities of varying chip usage [1][2] - The rise of domestic computing power is expanding the available resource pool, with domestic computing power now accounting for nearly half of the overall deployment, particularly excelling in inference and certain training scenarios [7] Group 2: Business Model and Services - Wunwen Xinqiong's business model is characterized by a "universal" approach, aiming to provide precise support for small and medium-sized enterprises during their growth phases through flexible computing power services [6][8] - The company offers various billing methods for its services, including per card, per hour, or based on usage volume, which transforms fragmented computing resources into standardized services [8] - The product matrix includes "large box," "medium box," and "small box" offerings, focusing on national-level computing power scheduling and integrating computing service capabilities into AI clusters and terminal devices [9] Group 3: Industry Trends and Future Outlook - The AI computing power industry is transitioning from a "technical dividend period" to a "value closed-loop period," with the core issue shifting from whether AI is usable to whether it is worth using, making cost-effectiveness a critical breakthrough point [10] - The company is exploring the customization of high-performance, cost-effective dedicated chips for edge AI applications, driven by the vast user base and manufacturing capabilities in China [10][11] - The construction of an ecosystem that connects models, systems, and hardware is a long-term goal, with the company aiming to create a positive cycle of hardware iteration, model optimization, and scene implementation [11]