In recent discussions, industry experts have pointed out that while the demand for artificial intelligence (AI) systems continues to rise, the demand for high-performance computing (HPC) appears to be declining. This has sparked a debate about whether AI is replacing HPC. However, many experts believe that this view is wrong because AI is actually a subset of HPC rather than a replacement for it.
High-performance computing is not a single application or technology area, but a broad term covering multiple fields, including financial services, pharmaceuticals, manufacturing, etc. These workloads require significant investments of time and capital due to their complexity and computational requirements. Although AI has experienced explosive growth in recent years, it still belongs to the category of HPC.
Both AI and traditional HPC workflows require high-performance infrastructure to meet the time and accuracy requirements of the solution. While it is possible to run some applications on a regular laptop, the time and resources required would render the results unrelevant. As the demand for higher accuracy and larger problems continues to increase, both HPC and AI need to continuously expand their computing capabilities.
Some people may think that AI uses accelerators such as GPUs, but many HPC applications do not, so they are different. However, HPC is not a specific application, but a field that covers a variety of technical operations. Many HPC users may not use the term HPC to describe their work, but their applications and equipment are still of the HPC type.
With the advancement of AI technology, almost all applications and workflows will be integrated with AI in the future. From drug discovery to manufacturing, AI will be integrated into traditional HPC applications. In addition, many emerging areas, such as personalized health predictions, improving agriculture and network security, will also require HPC infrastructure to support their development.
Currently, the demand in the HPC market has not decreased, but has expanded with the rise of AI. Data centers need to redesign their IT infrastructure to support new AI workloads and applications, which will become an important challenge in the future. According to estimates, current server racks consume about 15-18 kW when fully loaded. In AI applications, the power consumption of a single 8U node can reach 10 kW or even as high as 40 kW, which poses a huge challenge to the design of data centers.
Therefore, when a data center adds AI infrastructure, it first needs to check whether its power supply is sufficient and consider the power consumption of existing equipment. After conducting a system audit, many data centers may discover that 20% of their power consumption is wasted. In addition, cooling capacity is also an important consideration. Modern computing equipment can run at higher temperatures, and liquid cooling technology may become a future trend.
In summary, as the demand for AI and HPC continues to grow, data centers must improve their IT efficiency, ensure workloads are running on optimal configurations, and continuously monitor efficiency indicators to respond to changing market demands.
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