Explore how AI innovation and sustainable data center management intersect, focusing on energy-efficient strategies to balance performance and environmental impact.
With all that’s being said about the growth in demand for AI, it’s no surprise that the topics of powering all that AI infrastructure and eking out every ounce of efficiency from these multi-million-dollar deployments are hot on the minds of those running the systems. Each data center, be it a complete facility or a floor or room in a multi-use facility, has a power budget. The question is how to get the most out of that power budget?
Key Challenges in Managing Power Consumption of AI Models
High Energy Demand: AI models, especially deep learning networks, require substantial computational power for training and inference, predominantly handled by GPUs. These GPUs consume large amounts of electricity, significantly increasing the overall energy demands on data centers. AI and machine learning workloads are reported to double computing power needs every six months. The continuous operation of AI models, processing vast amounts of data around the clock, exacerbates this issue, increasing both operational costs and energy consumption. Remember, it’s not just model training, but also inferencing and model experimentation which consume power and computing resources.
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