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Zhitong Finance APP was informed that Haitong Securities released a research report saying that AI big model training has less demand for temporary rapid expansion of computing power, and private clouds can also shoulder computing power requirements. At the same time, for continuous and large-scale AI training, private cloud has higher efficiency and lower cost. From a completely visible point of view, with the help of private cloud, enterprises can completely control and visualize their network security status, and can customize it to meet their specific needs. The bank believes that the high security of private cloud makes it a more suitable solution. Suggested attention: Pingao (688227.SH) and Qingyun Technology (688316.SH).

The main points of Haitong Securities are as follows:

AI big model training has less demand for temporary rapid expansion of computing power, and private cloud can also shoulder the computing power requirements.

In the past, China’s public cloud market has always occupied the mainstream position of cloud computing. According to China ICT Institute, in 2021, the scale of China’s public cloud market reached 218.1 billion yuan, while the private cloud market was only 104.8 billion yuan in the same period. In the past, the development of the public cloud market was mainly due to the rapid growth of the demand of Internet enterprises in China in recent years, and the process of traditional enterprises going to the cloud was accelerated, while the public cloud had the advantage of flexibility and easy expansion. The bank believes that in this context, enterprises’ demand for cloud is increasing, while public cloud is easier to expand and more suitable for high-speed growth enterprises. The bank believes that in the context of the current rapid development of AI, the demand for rapid expansion of cloud servers is not so strong; Moreover, from another point of view, before the large-scale model training, there is often a preliminary understanding of the training computing power required by the model, and the required computing power will be prepared and laid out in advance, so it is unlikely that a large amount of computing power will be temporarily expanded during the model training, which makes the advantage that there is a cloud that can rapidly expand no longer exist in the field of AI large models, and the private cloud can also support the computing power demand of model training.

For continuous and large-scale AI training, private cloud has higher efficiency and lower cost.

When building a private cloud, the limited budget can be used efficiently by carefully planning hardware, capacity, storage and network configuration. On the other hand, although the public cloud has some advantages in ease of use, these services have great "binding" characteristics. For example, if users use Microsoft’s pre-trained DNN for image processing, they can’t easily run the generated applications on their own servers, and users can’t use Google’s TPU and AuoML tools in non-Google public clouds. However, in the current training of large models, it is necessary to constantly use new data and functions to keep its "freshness". Because the private cloud is only used by a single organization, the enterprise can completely control its software and hardware selection. This high degree of control means that the owner of the private cloud can reconfigure or customize the cloud resources for the task, further improving efficiency. As the big model becomes more and more complex, private clouds can provide greater generality and finer specifications (such as plugging in specific applications and ensuring continuous availability and data speed independence). The bank believes that on the one hand, the cost of public cloud layout and private cloud layout is basically the same for the demand of fixed pre-trained AI big model. Because there is no demand for rapid expansion, the scale cost advantage of public cloud will be weakened; On the other hand, the training efficiency of public cloud will be lower than that of private cloud specially built for its own AI training. For continuous and large-scale deep learning, using local private cloud can save a lot of costs and improve training efficiency.

Al training needs massive and highly sensitive data, and the high security of private cloud makes it a more suitable solution.

For the public cloud, when companies store their data and information in the cloud, it is difficult to ensure that these data and information will be adequately protected. The huge scale of the public cloud and the diversity of companies covering users also make it a favorite target for hackers to attack. In addition, there is the problem of hardware sharing in public cloud. Through public cloud, the work between different companies will be carried out on the same server, and this sharing mode is likely to lead to the disclosure of confidential data and information. Unlike public cloud, private cloud is a cloud infrastructure specially built to provide "isolated access" in a "single tenant environment", that is, it can only be accessed by a single entity, which is usually an enterprise that uses and maintains the cloud. The only purpose of establishing a private cloud is to provide services for enterprises that own the cloud. From the control point of view, because the owner completely controls the physical computing, storage and network equipment, the data security is promoted to the highest level, and the internal administrator has greater flexibility in implementing and accessing security tools; From a completely visible point of view, with the help of private cloud, enterprises can completely control and visualize their network security status, and can customize it to meet their specific needs. Because AI training needs a lot of data, for example, the data volume of GPT-3 pre-training reaches 45TB; Moreover, the application of artificial intelligence faces great security risks. On the one hand, data is associated with user privacy information, on the other hand, the destruction of the model will lead to decision-making errors, data poisoning will affect the effectiveness of intelligent services, deep forgery will be used for extortion, and data leakage will lead to user privacy exposure. Events occur frequently.Therefore, there will be higher requirements for security. Therefore, the bank believes that a private cloud with high security features is a more suitable solution.