第七届AI大模型行业应用落地论坛举办：算智双突围，落地正当时

Core Insights - The forum focused on the theme of "breaking through technical bottlenecks and creating intelligent life" in the context of AI large model applications, highlighting the importance of computational power and ecosystem building [1] Group 1: Key Themes and Discussions - The chairman of Parallel Technology, Chen Jian, emphasized that performance can be directly converted into revenue, showcasing how their software helps users identify program bottlenecks and optimize computational resources [3] - Zhihua Wang, co-founder and chief architect of Zhihua Huazhang, introduced the latest open-source achievement GLM-4.5, which boasts 300 billion parameters and delivers performance at one-third the cost of competitors [3] - Baidu's general manager of hybrid cloud, Du Hai, presented a case of a domestic single cluster with 32,000 cards, highlighting the transition from single-machine to super-node computing [3] Group 2: Challenges in AI Implementation - Li Dong from Tsinghua Chang Geng Hospital discussed the challenges of implementing AI in healthcare, emphasizing the need for safety, compliance, and cost management before pursuing disruptive innovation [4] - Wang Haoqi from 360 Group highlighted the necessity of security intelligence, sharing stories of security breaches and introducing a "Nano AI SaaS" aimed at small and medium enterprises [4] Group 3: AI Applications and Innovations - Liu Chenxi from Tencent Cloud presented the company's AI achievements, detailing the integration of AI with existing business scenarios and showcasing the Tairos visual model's capabilities [5] - Tian Yunlong from Haier Smart Home introduced the world's first family-oriented model matrix, demonstrating how small models can provide proactive services in household appliances [6] Group 4: Future Directions and Industry Insights - He Wanqing from Qingcheng Jizhi discussed the importance of optimizing domestic chips and shared the performance improvements achieved with their "Chitu" inference engine [6] - The forum concluded with a consensus that there is no universal solution for large model implementation; instead, specific scenarios must be addressed through engineering methods [6]