In the past year, a variety of big models have flourished, and AI has received unprecedented popularity. However, the general big models are still far from the industry. When discussing the direction of implementing large models, many professionals are targeting the financial industry, stating that large models will bring a second wave of digitalization in the financial industry. The financial industry has accumulated a large amount of high-quality data. There are billions of users on various financial platforms, with a vast array of user profile data and transaction data. The analysis and processing of the above data using large models can improve financial efficiency. For example, financial institutions can predict user behavior preferences and more efficiently and accurately assess customer risks; AI can also monitor trading and market fluctuations in real-time, and formulate strategies in a timely manner. In fact, the financial industry is also actively embracing big models. A survey by IDC (International Data Corporation) shows that over half of financial institutions plan to invest in generative artificial intelligence technology in 2023, and only 10% of financial institutions indicate no experimental plans. Not long ago, two domestic technology companies jointly launched the Volcano Intelligent Spectrum High Performance Financial Big Model, which also actively paved the way for the landing of technology finance AI. The prospect of large models is certainly promising, but the financial industry has extremely high requirements for security and privacy. In the process of promoting the implementation of large models in the financial industry, safety and compliance are the biggest technical challenges. Developing financial AI is a systematic project that integrates technology and industry. From the collaborative research background of the volcano intelligent spectrum high-performance financial model, it can be seen that on the one hand, the upgrading and iteration of model performance, functionality, and underlying architecture are the foundation; On the other hand, efficient computing infrastructure, an open and secure ecosystem, rich financial industry practices, and comprehensive delivery guarantees are all important prerequisites. The big model is a battleground for the financial industry. But if AI is further integrated into financial core businesses such as risk control, it still needs to be adapted in vertical fields and tested over time. The financial industry generally believes that the easiest to implement at present, including AI investment advisors, automated customer service, risk assessment, automated report generation, code generation applications, etc., should start from the periphery and gradually approach the core. The impact of big models on the financial industry is bound to surpass the previous wave of digitization, and the transformation of financial work models is unstoppable. However, the final mile of implementing large models in financial scenarios is full of variables. Only by solidifying the foundation of technology can future AI finance achieve stability and progress. (Lai Xin She)