证券研究报告：计算机|点评报告 发布时间：2025-11-24 行业投资评级 强于大市|维持 行业基本情况 | 收盘点位 | | 5068.36 | | --- | --- | --- | | 52 | 周最高 | 5841.52 | | 52 | 周最低 | 3963.29 | 行业相对指数表现（相对值） -13% -9% -5% -1% 3% 7% 11% 15% 19% 23% 27% 2024-11 2025-02 2025-04 2025-06 2025-09 2025-11 计算机 沪深300 资料来源：聚源，中邮证券研究所 研究所 ⚫ AI 时代需要 AI 容器技术，华为 Flex.ai 对标英伟达 Run:ai 具有独特优势 传统容器技术难以适配 AI 工作负载需求，AI 容器作为轻量级虚 拟化技术，可打包模型代码与运行环境实现跨平台迁移，解决环境配 置不一致问题，且能按需挂载 GPU/NPU 算力、优化集群资源利用率。 Gartner 表示，目前 AI 负载大多都已容器化部署和运行，据预测，到 2027 年，75%以上的 AI 工作负载将采用容器技术进行部署和运行。 与英伟达今年年初收购的 ...

Core Insights - The rapid development of the AI industry is creating a massive demand for computing power, but the low utilization rate of global computing resources is becoming a significant bottleneck for industry growth [1] - Huawei's new AI container technology, Flex:ai, aims to address the issue of computing resource waste by allowing a single GPU/NPU card to be divided into multiple virtual computing units, improving resource utilization by 30% [1][2] - Flex:ai is positioned to compete with Nvidia's Run:ai, focusing on software innovation to unify management and scheduling of various computing resources without hardware limitations [2] Group 1 - Flex:ai technology can split a single GPU/NPU card into virtual computing units with a precision of 10%, enabling multiple AI workloads to run simultaneously [1] - The technology has been validated in real-world applications, such as the RuiPath model developed in collaboration with Ruijin Hospital, which improved resource utilization from 40% to 70% [3] - Gartner predicts that by 2027, over 75% of AI workloads will be deployed and run using container technology, indicating a shift towards more efficient resource management [3] Group 2 - Flex:ai will be open-sourced in the Magic Engine community, contributing to Huawei's comprehensive ModelEngine open-source ecosystem for AI training and deployment [3] - Unlike Run:ai, which primarily serves the Nvidia GPU ecosystem, Flex:ai supports a broader range of computing resources, including both Nvidia GPUs and Huawei's Ascend NPUs [2]

Core Insights - Nvidia is experiencing challenges in its enterprise software sales as it attempts to onboard large clients in regulated industries while maintaining its growth trajectory amid the AI boom [1][2] Group 1: Software Sales Challenges - Internal communications reveal that Nvidia's sales team is struggling to present a unified message regarding its software offerings alongside its AI hardware [2][7] - The company is focusing on selling Nvidia AI Enterprise (NVAIE) and other software products, but there is a need for a comprehensive narrative to effectively communicate these offerings to clients [4][6] - A July email indicated that stand-alone software sales are projected to exceed targets at 110%, while software sold with hardware is only expected to reach 39% of its goal [6] Group 2: Client Education and Legal Concerns - There is a significant disconnect between Nvidia and its clients' legal and procurement teams, particularly in understanding the software sales processes during negotiations [8][9] - The company is planning workshops to educate clients on NVAIE and other products, addressing the need for better internal and external education [7][8] - Data security and indemnity obligations are highlighted as major negotiation sticking points, with clients requesting higher damages caps than Nvidia is comfortable with [9] Group 3: Market Position and Future Outlook - Despite the challenges, Nvidia is forecasting strong software sales, with NVAIE expected to hit 186% of its sales target for the quarter [6][5] - The company’s software segment, while smaller, is crucial for generating recurring revenue and increasing customer dependence on its AI products [5]