The cost and complexity of deploying large-scale GPU clusters for generative AI training will drive many enterprises to the public cloud. Most enterprises will use pre-trained foundation models, to reduce computational overheads.
Not all generative AI applications will require large and dense infrastructure footprints. This complicates AI power consumption projections and data center planning.
Enterprises have much enthusiasm for AI, interviews and workshops by Uptime Intelligence suggest, but this is tempered by caution. Most hope to avoid disruptive, expensive or careless investments.
While the aim of FinOps is to manage just the cloud costs, technology business management seeks to aggregate all costs of IT, including data centers, servers, software and labor, to identify savings and manage return on investment.
Enterprises have various options on how and where to deploy their AI training and inference workloads. This report explains how these different options balance cost, complexity and customization.
To meet the demand driven by AI workloads, a new breed of cloud provider has emerged, delivering inexpensive GPU infrastructure as a service. Their services are highly demanded today, but longer-term, the market is ripe for consolidation.
Cybersecurity strategies often evolve organically: tools are added, requirements change, and the result is a lack of coherent structure. Cybersecurity professionals can benefit from adopting frameworks to organize these activities
Many speculate that AI and automation might replace humans in data center jobs but there is little evidence to support this line of thought. The data center industry has characteristics that researchers believe may protect against worker displacement
The Netherlands is now enforcing the energy saving obligation, suggesting they will also enforce EED energy management system and audit requirements. Data center operators need to establish a plan to optimize their energy performance.
Uptime Institute's 2024 IT and Power Efficiency Survey (n=941) focuses on strategies for managing data center IT equipment inventories, gathering data for accuracy and completeness, and the collection of storage and server information.The attached…
This report provides a regional view of the results from the Uptime Institute Global Data Center Survey 2024 and highlights some of the different challenges and strategies of data center owners and operators across the globe.
Examining staffing trends in China’s data centers could provide insights into how US and European data center teams can expand their talent pipeline and address workforce shortages caused by an aging population.
Software for data center management and control is well established, if not always widely used. Some operators are also benefitting from integrated facility and IT software.
Pulling reliable power consumption data from IT is increasingly important for operators. Although third-party software products offer promise, significant roadblocks still hinder adoption.
Trust in AI as a tool for data center operations has declined sharply in the past three years. It is possible to control for the factors that drive mistrust — and see better outcomes when employees interact with AI-based systems.
Owen Rogers
Max Smolaks
Andy Lawrence
Michael O'Neil
Antonio Ramos
Rose Weinschenk
Jay Dietrich
Anthony Sbarra
Laurie Williams
Douglas Donnellan
John O'Brien