1月6日，粤港澳大湾区生成式人工智能安全发展联合实验室珠西中心、珠海服务站揭牌仪式在珠海具身 智能创新中心举行。一批珠海本地大模型备案企业同步在活动现场获颁"生成式人工智能服务备案证 明"。 当人工智能成为驱动新质生产力发展的核心引擎，毗邻港澳、坐拥经济特区独特优势的珠海，正以前瞻 性布局、系统性举措，在人工智能赛道上走出一条特色发展之路。 算力支撑方面，当前珠海本地成规模智能算力近7000P，未来规划规模将达数万P，同时推出5亿元"算 力券"等优惠政策，为企业提供算力保障。政策保障方面，珠海充分运用经济特区立法权，颁布《珠海 经济特区数据资源开发利用条例》，为AI企业提供安全合规便利的数据支持。人才储备方面，珠海集 聚3所985高校校区，15万在校大学生提供充沛人才支撑。区位协同方面，作为全国唯一陆路连通港澳的 城市，珠海依托跨境网络基础和粤港澳大湾区协同优势，为AI产业出海和跨境创新合作开辟广阔空 间。 据悉，此次揭牌的粤港澳大湾区生成式人工智能安全发展联合实验室珠西中心、珠海服务站将重点聚焦 筑牢跨境安全协同防线、赋能特色产业安全发展、构建开放创新生态体系三个方向发力。 对于珠海AI产业的发展前景，企业 ...

Core Insights - The rapid advancement of AI technologies, particularly with breakthroughs like DeepSeek, is leading to a significant acceleration in the iteration of AI applications, especially in the financial sector, which is poised to become a leading example of deep integration of large model technologies [1] - There are notable differences in the development of large models between domestic and international financial institutions, with international players often opting for commercial models while domestic institutions focus on open-source or self-built models [2] - Industrial banks, such as ICBC, are developing a "1+X" application paradigm for large models, which aims to enhance business capabilities through a dual integration of technology and business [3] Domestic and International Trends - International financial institutions tend to purchase external commercial large models and utilize public cloud deployment, while domestic institutions prefer self-built or collaboratively developed models with private cloud deployment [2] - The application scenarios in international finance are more diverse, focusing on core business areas like sentiment analysis and risk assessment, whereas domestic institutions are initially targeting efficiency improvements for frontline employees [2] New Application Models - ICBC has established a "1+X" model that includes a financial intelligence core and various capabilities such as knowledge retrieval and data analysis, enabling over 200 application scenarios [3] - The model allows for significant innovation in business processes, transitioning from single-scene empowerment to comprehensive business restructuring [3] Future Trends - Large models are expected to evolve into foundational infrastructure for financial services, with advancements in computing power supporting a "cloud-edge-end" AI deployment model [4] - The development of a model matrix layout is anticipated, featuring one versatile base model complemented by multiple specialized models for specific financial scenarios [5] - Regulatory bodies are expected to introduce clearer standards and guidelines for the ethical and compliant use of AI technologies in finance [6] Challenges in Implementation - Financial institutions face challenges in balancing the costs and value of AI model applications, as the demand for computational resources continues to rise [7] - The slow accumulation of high-quality data poses a significant barrier to achieving optimal AI performance, as the effectiveness of AI applications is increasingly dependent on data quality [7] - There is a notable shortage of interdisciplinary talent capable of bridging the gap between finance and AI technology, necessitating the establishment of robust talent development systems [7] Strategies for Smaller Institutions - Smaller financial institutions are encouraged to adopt a mixed model of "external collaboration + lightweight adaptation" to effectively leverage large models [9] - Focusing on core business areas and creating benchmark application scenarios can help smaller institutions maximize their resources [9] - Building a lightweight data ecosystem through distributed collaboration can address data limitations faced by smaller institutions [9] Future Development Pathways - Financial institutions should aim to enhance their intelligent infrastructure and develop a layered technical architecture to address the complexities of model development and computational infrastructure [10] - Accelerating the iteration of specialized models in vertical fields will enhance competitive advantages in core financial areas [10] - The integration of large model technologies is seen as a key driver for advancing financial services from process optimization to cognitive transformation [10]