CASE STUDY examine efficient and scalable deployment of smaller , specialised models , as well as caching for model inference , where responses , or intermediate inference results leading to them can be cached and reused in processing similar prompts .
As AI continues to evolve , the need for energy-efficient solutions will only grow . By leveraging specialised architectures and embracing innovative software design , forward-looking organisations in the UAE can significantly lower the energy demands associated with AI applications .
This not only alleviates the environmental impact but also enhances the economic viability of AI technologies , enabling wider adoption and , ultimately , the achievement of the nation ’ s long-term sustainability goals . p
Walid Issa , Senior Manager , PreSales and Solutions Engineer , Middle East and Africa , NetApp
Energy management inside AI data centres
The growth of hyperscale data centres , edge computing , and hybrid cloud adoption are driving demand for efficient energy management . Data centres are significant drivers of growth in electricity demand in many regions , especially now with the rise in AI . Global electricity consumption of data centres in 2022 was an estimated 460 terawatt-hours , TWh , and it could reach more than 1000 TWh in 2026 .
Top trends include the adoption of renewable energy sources , advanced cooling technologies , and automation to optimise operations . Companies are also integrating energy-efficient hardware and virtualisation strategies to manage power consumption , ensuring sustainability aligns with scalability .
CIOs can prioritise energy-efficient technologies like flash storage and virtualisation to minimise hardware footprint . Transitioning to renewable energy sources and integrating carbon-neutral power options can significantly lower emissions . Leveraging AI to optimise cooling systems and workload distribution ensures efficient energy usage .
Additionally , modernising legacy infrastructure with scalable , modular designs and adopting cloud-based solutions can enhance flexibility while reducing environmental impact .
CIOs can utilise workload orchestration to align applications with the most energy-efficient resources , such as scheduling tasks during off-peak energy hours . Migrating workloads to cloud environments powered by renewable energy reduces on-premises energy use .
Employing containerisation and serverless computing minimises resource wastage . Data deduplication , compression , and tiering ensure storage efficiency . Additionally , monitoring carbon usage effectiveness , CUE helps CIOs evaluate and optimise application energy impact , fostering a culture of sustainability across IT operations .
CIOs should also implement robust monitoring to track and improve sustainability metrics continuously . Innovations include AI-powered energy management , liquid cooling systems , and advanced storage technologies . NetApp contributes by offering AFF A-Series and ASA All-flash SAN storage , which consolidates workloads and reduces energy demand .
Tools like the BlueXP Sustainability Dashboard empowers businesses to monitor and optimise energy efficiency . Additionally , NetApp ’ s sustainability-focused packaging and e-waste management further support reducing environmental impact across the data lifecycle .
NetApp ’ s advanced storage solutions , such as all-flash arrays , can help reduce energy use while delivering high performance . With tools like Cloud Insights , NetApp helps customers gain visibility into their data centres ’ energy efficiency , empowering informed decisions to lower their environmental impact .
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