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.
Generative AI is not only accelerating the adoption of liquid cooling but also its technical evolution. Partly due to runaway silicon thermal power levels, this has led to a convergence in technical development across vendors.
Hydrogen from renewable sources is in short supply. While future plentiful supplies are planned, currently only a very small number of data centers are using hydrogen for standby power.
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.
The UNEP U4E initiative has proposed guidelines for data center design and operation and server and storage product efficiency requirements. These have far-reaching implications for data center operations in developing countries.
Results from the Uptime Institute Security Survey 2024 highlight the different cybersecurity approaches used by operators against a widening range of threats.
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.
New augmented reality and virtual reality technologies can provide effective training capabilities for data center staff but are not yet a complete substitute for in-person training.
Many organizations still do not tap into the potential power efficiency gains hidden in servers. Without operational focus on extracting those, future server platforms may bring marginal, if any, energy performance improvements.
The number of proposals for new hyperscale-size data centers have reached new heights in 2024. Those that are built will require huge investment and resources — but many proposals will fail to move forward.
Staff shortages and recruitment challenges remain the key workforce challenges facing data center owners and operators in 2024. This report highlights some of the findings from the Uptime Institute Staffing and Recruitment Survey 2024.
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.
Data center operating and capital costs have been rising strongly in recent years — and will almost certainly continue to do so. Sooner or later, those in the IT supply chain will need to deliver their backers a return on investment.
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.