AI Services


AI Infrastructure Advisory

An AI data center is more than just a high-density facility. AI is an exacting application that makes specific demands on infrastructure—creating new challenges for design, construction, commissioning, operations, and maintenance. The stakes are high: delays, design flaws, or operational missteps can derail AI initiatives and erode competitive advantage.

Uptime Institute's AI Infrastructure Advisory provides independent technical oversight across the full lifecycle, ensuring your facility performs as intended from day one—protecting both your investment and time to market.

With over 30 years setting the global standard for data center performance, Uptime Institute brings unmatched expertise to AI infrastructure's unique challenges: rapidly increasing power densities, hybrid liquid and air cooling solutions, complex power profiles, and the high-performance operations that mission-critical workloads demand. Our vendor-neutral advisors help organizations accelerate delivery while protecting investments from design drift, construction deviations, and operational risks that can inflate costs and delay deployment.

We provide guidance from initial design and technology selection through commissioning and ongoing operations—minimizing execution risk while aligning infrastructure with business objectives. This alignment ensures facilities are ready for today's AI demands and adaptable for tomorrow's innovations, maximizing the return on your infrastructure investment.

Through five focused service areas, we help organizations prioritize decisions that ensure performance, scalability, energy efficiency, and long-term availability—enabling faster deployment without sacrificing resilience.

Whether you're designing and building new, retrofitting existing infrastructure, or evaluating technology vendors, Uptime Institute delivers the independent assurance that your facility will be ready to support your AI ambitions.

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AI Infrastructure Advisory

The AI Advisory provides five services tailored to your organization’s priorities. Each module starts with an immersive two-day Requirements and Objectives Workshop to gather information relevant to the module and share industry best practices. After the workshop, we provide guidance through a collaborative process, customized for each module.

Design Development and Review

Ensure that designs are optimized for AI workloads, performance, and scalability. Uptime will help you determine resiliency requirements, impacts to technology requirements, and align operational maintenance to business objectives.

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Technical Vendor Requirements and Evaluation

This module is designed to ensure that your technology strategy and solution requirements allow for scalable, high-performance AI environments. This ensures a comprehensive Data Center RFP and includes an evaluation of vendor responses to help document technical requirements for power, cooling, and a vendor evaluation framework.

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Construction Oversight and Validation

The construction module ensures that the installation and implementation of the AI design and equipment is aligned with the basis of design and client objectives. This will include a review of engineer of record comments and will help identify variations to client requirements for five (5) subsystems, and construction monitoring visits.

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Level 4 and 5 Commissioning

This module develops a comprehensive commissioning strategy with consideration for the impacts of different technology solutions. This will ensure that AI infrastructure systems perform as designed and are validated for operational readiness.

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Operations and Management Strategy

Uptime Institute will work with organization to develop a comprehensive operations strategy and plan, which will include qualifications, staff levels, and maintenance. This module will optimize ongoing performance, reliability, and efficiency of AI infrastructure operations.

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AI Infrastructure Planning Advisory

Uptime Institute provides a range of services to align with every stage of the AI journey. These include:

  • Help enterprises evaluate and select the optimal deployment strategy for their AI use cases.
  • Assist businesses to navigate the complex landscape of AI infrastructure technology by providing a comprehensive analysis of the required AI infrastructure, ensuring that businesses can effectively meet their intended AI use cases.
  • Assess, design, and validate a cloud environment tailored to your AI use cases.
  • Provide end-to-end RFP support services to assist in selecting colocation providers capable of hosting AI workloads with specific identified requirements.
  • Provide an onsite assessment of the current installed data center infrastructure and operations to identify the capabilities to meet the unique needs of AI workloads.
  • Assess the technical feasibility to meet the owner’s requirements for their planned AI data center.
  • Conduct design review to identify vulnerabilities in the AI design to meet the AI IT demand and resiliency objectives.
  • Ensure that the commissioning plan and script is comprehensive.
  • Conduct onsite assessment to ensure infrastructure and operations can perform to the unique requirements of AI workloads.

View Our Report Series on AI Topics

Electrical considerations with large AI compute

The training of large generative AI models is a special case of high-performance computing (HPC) workloads. This is not simply due to the reliance on GPUs — numerous engineering and scientific research computations already use GPUs as standard. Neither is it about the power density or the liquid cooling of AI hardware, as large HPC systems are already extremely dense and use liquid cooling. Instead, what makes AI compute special is its runtime behavior: when training transformer-based models, large compute clusters can create step load-related power quality issues for power distribution systems in data center facilities. A previous Intelligence report offers an overview of the underlying hardware-software mechanisms.
Electrical considerations with large AI compute

AI power fluctuations strain both budgets and hardware

AI training at scale introduces power consumption patterns that can strain both server hardware and supporting power systems, shortening equipment lifespans and increasing the total cost of ownership (TCO) for operators.
AI power fluctuations strain both budgets and hardware

AI embraces liquid cooling, but enterprise IT is slow to follow

The enthusiasm for generative AI is attracting serious investment, and the associated power and cooling requirements will pose a significant challenge for the data centers that house it. Upcoming AI training clusters will escalate silicon power and rack density to unprecedented heights, upending infrastructure design conventions and accelerating the adoption of cold plate and immersion cooling systems.
AI embraces liquid cooling, but enterprise IT is slow to follow

Neoclouds: a cost-effective AI infrastructure alternative

Over the past decade, three tech giants have solidified their dominance in the cloud computing market: Amazon Web Services (AWS), Microsoft Azure and Google Cloud. Estimates suggest that by the end of 2023, these companies collectively controlled around two-thirds of global cloud spending, a notable increase from 47% in 2016.
Neoclouds: a cost-effective AI infrastructure alternative

Generative AI and global power consumption: high, but not that high

Uptime Intelligence has been asked more questions about generative AI and its impact on the data center sector than any other topic. The questions come from enterprise and colocation operators, suppliers of a wide variety of equipment and services, regulators and the media.
Generative AI and global power consumption: high, but not that high

The following resources on AI topics are available to our Uptime Network and Intelligence members:

Uptime Intelligence
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