AI and the Data Center: Challenges and Investment Strategies
AI applications must be supported by massive compute power, which means larger and more plentiful data centers, but growth will be hindered by lack of available transmission power.
The uptick in AI usage is causing rapid growth in the data center market to accommodate the explosion of data these technologies are creating.
Adding AI to the already massive pool of available technology, including internet of things (IoT) devices, will generate even more customer data, leading to an exponential increase in data volumes.
The bottom line is that all this data needs to reside somewhere, and organizations will turn to data centers.
Kevin Shtofman, head of innovation at Cherre, explains AI will create increased demand for computing power, requiring investment in AI-specific hardware, adoption of new data center designs, and exploration of emerging technologies such as edge computing.
"AI applications require massive amounts of computing power, especially for training complex deep learning models," he says. "As AI becomes more widespread, the demand for computing power will increase, driving the need for more data centers to support this growth."
The adoption of AI will also increase data storage needs, as AI-driven applications require vast amounts of data to train and improve models.
"This data must be stored and accessed quickly, which requires significant amounts of storage capacity," he says. "As a result, data centers will need to expand their storage capabilities to accommodate the growing demand."
Shtofman adds as AI applications become more widespread, there is a growing need for real-time processing and decision-making.
"This has led to the rise of edge computing, which involves processing data closer to the source rather than sending it to a centralized data center," he says. "As a result, more data centers will need to be built closer to the edge to support this trend."
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