智通财经网·2025-12-24 06:49Group 1 - AI Infra has become a key "seller" for application deployment, with computing scheduling being the core variable determining the profitability of model inference [1] - Domestic model token fees are significantly lower than overseas, leading to higher cost sensitivity; for instance, Alibaba's Aegaeon can reduce GPU usage by 82% through token-level scheduling [1] - The combination of generative AI and agents is accelerating penetration, with AI infra software expected to enter a high growth phase [1] Group 2 - The demand for data infrastructure is surging ahead of application explosion, with vector databases becoming a necessity; Gartner predicts that by 2025, enterprise adoption of RAG technology will reach 68% [2] - The data architecture in the AI era is shifting from "analysis-first" to "real-time operations + analysis collaboration," leading to significant changes in the industry [3] - MongoDB is well-positioned to meet the low-cost AI deployment needs of small and medium-sized clients, achieving a 30% growth rate in its core products for FY26Q3 [3] Group 3 - NVIDIA has introduced a SCADA solution that connects GPUs directly to SSDs, reducing IO latency to microsecond levels, which is crucial for vector databases to adapt to AI real-time inference needs [4] - Relevant companies in this space include MongoDB, Dameng Data, Yingfang Software, Snowflake, and Deepin Technology [5]