AI Computational Power Dictates Everything in Infrastructure Design
Artificial Intelligence (AI) demands a new class of compute power requirements. In the past 10 years, the average power consumption of data center processors and accelerators has nearly tripled—and the requirements continue to climb.
AI Computational Power
Scalability Pain Points
The increased power consumption required for graphics processing unit (GPU) clusters within AI infrastructure has significant implications for data center operations. Compared to traditional IT systems, AI clusters have demanding and complex power requirements where high-density racks can range from 40kW to 125kW and extreme density racks can reach 200kW or higher.
Strain on Power Grids
The need for constant, high-density power burdens existing critical infrastructure and can slow the transition to clean energy sources.
Environmental Concerns
Absent advancements in energy-efficient technologies, expanding energy demands hinder environmental and climate-neutrality goals.
Rising Energy Costs
AI inference and model training can be extremely energy-intensive, raising more concerns about sustainability and energy costs.
Compute Bottleneck
Memory can be a significant bottleneck to compute capacity if the processor needs to access data faster than it can be delivered.
AI Workloads are Driving a Massive Increase in Power Consumption
AI is rapidly revolutionizing industries, and with this accelerated growth comes significantly increased demands for energy. As AI workloads expand, the power consumption necessary for training and running models skyrockets, raising concerns about sustainability and climate impact.
By 2026, AI data centers alone are expected to consume 90 TWh each year—a tenfold increase from 2022. Moreover, data centers that house servers for AI operations are responsible for over 1% of global electricity use and are projected to consume up to 12% of U.S. electricity by 2028.
Globally, data center energy consumption continues to surge and is projected to exceed 1,300 TWh by 2030 unless new efficiency improvements materialize.
Environmental Implications
The energy-intensive nature of AI workloads poses a substantial challenge to an organization's climate goals. Although many tech giants pledged carbon neutrality by 2030, greenhouse gas emissions continue to grow exponentially due to data center expansion and related AI computational power requirements.
Beyond their carbon footprint, data centers place strain on other local resources as well. These facilities consume millions of gallons of drinking water each year, which poses a major challenge for water-scarce regions. Meanwhile, communities located near data centers are raising concerns about electricity, water access, and zoning issues as jurisdictions allocate more real estate for data centers located near population zones.
Innovative Sustainable Solutions for Scalability
Power and heat are two of the biggest challenges facing data centers today. More power is needed for higher density racks, but these racks then produce amounts of heat that cannot be cooled sustainably using traditional methods.
Liquid immersion cooling offers a compelling solution to this challenge. This approach requires considerably less power compared to traditional air cooling systems. By eliminating fans, reducing space requirements, and removing overhead for cooling infrastructure, liquid immersion cooling can yield up to 50% reduction in the energy required to cool a data center's server equipment.
Case Study: Learn how we boosted performance and lowered emissions at Shell's data center with data center immersion cooling.
Penguin Solutions can also help you improve the sustainability of your data center with solutions such as waterless cooling via direct-to-chip cooling and two-phase liquid cooling. Discover our scalable AI infrastructure solutions to unlock the full usage and potential of your AI solution.
Computational power in AI refers to the processing capacity needed to train and run AI models. High-performance computing enables faster insights, larger models, and more accurate predictions.
AI systems require scalable computational power to handle increasing data volumes, train complex models, and support real-time decision-making in areas like machine learning and deep learning.
Industries like life sciences, financial services, aerospace, and weather modeling rely heavily on scalable compute infrastructure for data processing, simulations, and real-time analytics.
By leveraging purpose-built infrastructure, workload-specific accelerators, and edge-to-cloud scalability businesses can optimize AI workload performance while controlling energy costs.
Talk to the Experts at Penguin Solutions
Reach out today and learn how we can help you address your scalable AI computational power needs as you design your data center infrastructure.