Market researchers at Markets and Markets predict that the AI rack-mounted servers market will reach an impressive $407 billion in global sales by 2027, a significant increase from $86.9 billion in 2022. According to BITKOM, 68 percent of German companies see great potential in AI. However, 43 percent of companies surveyed believe they are lagging in the use of AI in their daily work, and 38 percent even believe they have completely lost touch. There's a lot of work to be done, and fast. But how do companies get there and create the necessary IT infrastructure?
Data centers need to meet the future demands of rack-mounted servers AI applications, including GenAI, autonomous driving, intelligent diagnostics, algorithmic trading, and intelligent customer service. More and more data generation and the growing need for computing and faster data transfer are putting tremendous pressure on older IT infrastructures. Existing IT architectures are often ill-suited to the rapidly growing volume of data and AI, as the development and deployment of AI models present several challenges for data centers.
Development and implementation of artificial intelligence applications
In large-scale computing, the efficiency of a single node is very limited. Therefore, system interconnection, algorithms, and interconnection optimization are becoming more and more important. A rack-mounted servers system-centric approach to building IT infrastructure is best suited to overcoming obstacles in AI adoption. When deploying AI, the focus should be on the entire system, including the coordination of algorithms, computing power, and data. By integrating compute resources, data resources, R&D deployment environments, and process support, you can improve the efficiency and stability of AI development and implementation from cluster management to training development and inference applications, thereby expanding the innovation path through full-stack optimization.
This system-centric approach is necessary because different groups of people, including infrastructure managers, data scientists, and business people, work together in AI development and applications. IT infrastructure experts attach great importance to cluster stability and the optimal use of computing resources. Data scientists focus on the efficiency and stability of model training. Business professionals care about reasoning and want simple deployment of services and flexible computing resources. Throughout the AI rack-mounted servers process, companies can improve the efficiency and stability of the entire cluster through systematic design, so that companies can consistently gain business insights, generate revenue, and remain competitive.