On the server? Which infrastructure environment is the best to deploy in the IDC configuration package of the server room? Today, enterprise organizations are using AI applications to analyze the huge amount of data that is growing exponentially on a large scale. This requires not only extremely high requirements, but also powerful parallel processing functions. Therefore, the original standardized CPU can no longer fully perform many AI resolution tasks. In view of this, what effective measures should be taken when the enterprise data center approaches or reaches the server performance bottleneck?

The enterprise data center prepares and responds to the limitations of Ai applications for the enterprise's local deployment environment and cloud infrastructure. At the same time, we also interviewed colleagues in the data center industry and invited them to provide relevant guidance and suggestions, including emphasizing the importance of close cooperation between enterprise data centers and suppliers. These suppliers can provide corresponding guidance for your enterprise to transition from early stage to stable advanced production stage as soon as possible, so as to make full use of the functions of AI.

At present, enterprises and organizations are actively working to deal with many variables to determine their position on the use of AI applications, which can provide new insights from in-depth learning. At present, this field can be said to be full of unlimited business opportunities, and failure to take positive action may become a business disaster, because competitors are collecting and analyzing massive data and information that were previously unavailable to expand customer base. Most enterprises have realized this serious challenge, so their business departments, it staff, data scientists and developers are working together to determine the enterprise's artificial intelligence strategy. To some extent, enterprises adopting AI strategies will gradually experience the past experience of leading enterprises in more advanced AI applications: their performance will encounter bottlenecks. AI applications, especially deep learning systems, can analyze massive data information that is growing exponentially. However, these systems have high requirements and require strong parallel processing capabilities, so more and more standardized CPUs can no longer fully perform these Ai tasks. AI users in the early and advanced stages will have to reinvent their server infrastructure to achieve the required performance at some point.

On the server? Which infrastructure environment is the best to deploy in the IDC configuration package of the server room? Idc suggested that enterprises that are developing Ai functions or expanding existing Ai functions should solve the bottleneck problem of server performance in a strictly controlled way. It is important to implement the next infrastructure migration on the premise of fully mastering the relevant details. In addition, we suggest that they must work closely with their suppliers. These suppliers of [Foreign Trade] can provide corresponding guidance for enterprise customers to transition from the early stage to the stable later stage of production as soon as possible, so as to make full use of Ai's functions. In order to break through the bottleneck of running Ai on the infrastructure of the data center, most enterprises' AI and deep learning applications have reached a certain level of "server infrastructure bottleneck restrictions" in poc testing or production mode.