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EnterpriseDB ("EDB"), a vanguard in sovereign AI and data solutions, has unveiled compelling new research confirming the impressive energy efficiencies and cost savings delivered by its sovereign data platform to enterprises. With a focus on EDB Postgres AI (EDB PG AI) implementations across three Fortune 500 financial firms, the findings highlighted a remarkable 81% reduction in energy consumption and an 87% dip in emissions.
The research underscores a significant shift, as over 95% of global enterprises express intentions to control their own AI and data setups by 2025, as per EDB Sovereignty Matters study. This transition is anticipated to intensify demands on energy grids and IT budgets. It’s already established that electricity expenditure constitutes 46% of total data centre spending and is expected to rise by a 45% compound annual growth rate until 2027, according to IDC.
As digital ambitions soar, concerns grow about the sustainability of current power infrastructures and economic frameworks. EDB's CEO Kevin Dallas eloquently elucidates that 83% of global enterprises currently rank power efficiency among their top three catalysts for reassessing data centre designs amid evolving agency AI paradigms.
Incendium Consulting's independent assessment has put a spotlight on EDB PG AI, highlighting its substantial contributions to data centre energy efficiency and sustainability. Covering major global financial entities, it revealed a more than 50% average drop in emissions due to EDB’s solutions, with certain organisations reporting a striking 94% reduction.
Complementing these innovations is the EDB Postgres® AI Efficiency Calculator, a newly launched tool that offers firms quick insights into their infrastructure optimisation benefits. This interactive solution quantifies the financial, performance, and environmental impacts of refining data estates, equipping businesses to make real-time adjustments akin to an adept database team.
EDB PG AI reduces costs and emissions through a host of dynamic features. Intelligent workload optimisation enables efficient handling of critical operations without additional manual efforts. Meanwhile, on-demand AI model servicing manages AI needs dynamically, eradicating underutilised infrastructure energy drain.
Moreover, EDB separates compute and storage tasks, independently scaling resources for analytical demands while shifting colder data to cost-effective storage solutions, further enhancing energy savings.
Collaboration with industry leaders, such as Supermicro, ensures access to the industry's best infrastructure, including liquid-cooled systems to cut energy usage, and adaptable GPU-optional environments catering to varying workload needs. This strategy embodies a future-ready AI framework, designed for power efficiency and customisability.
