Artificial Intelligence Data Centers

The Role of AI in Data Centers

AI data centers require advanced hardware, including graphics processing units, field-programmable gate arrays, and application-specific integrated circuits, to handle complex computational tasks efficiently.
Optimizing the infrastructure of data centers with AI-ready solutions involves utilizing various technologies, services, and strategies tailored for AI and machine learning workloads.
The design of AI data centers incorporates AI-optimized servers and storage systems specially adapted to meet the demands of high-density workloads.
High-density AI workloads in data centers necessitate significant resources, including space, power, cooling, and robust data/power port connections to operate effectively.
The expansion of AI workloads is projected to increase data center power demand by 160% by 2030, highlighting the growing importance of energy efficiency in AI-driven environments.

Evolution of AI Models in Data Centers

The evolution of AI models has transitioned from static and deterministic models to fully dynamic models capable of generating probabilistic outputs, enabled by advancements in technology and data availability.
This progression in AI modeling has been facilitated by declining costs in bandwidth and data storage, as well as compute capabilities, which have opened new avenues for AI development.
AI represents a significant paradigm shift for data centers, requiring reconfiguration of existing infrastructure to handle increased computing power, energy consumption, and cooling needs.
The rapid growth in AI workloads has resulted in heightened demands for computational power and lower latency from data centers, presenting both opportunities and risks within the sector.
Companies that strategically align technology with specific business drivers in data center investments will be better positioned to succeed amid the evolving landscape of AI-driven demand.

Market Structures Emerging from AI Demand

The emergence of AI capabilities with clear monetization potential is driving a competitive cycle in the market, leading to the investment, launch, competition, and potential consolidation of consumer and commercial AI products.
Current technical capabilities across market participants in the AI sector are similar, indicating that differentiation will likely arise from factors such as product/market fit and distribution strategies.
As AI demand grows, understanding who the main players will be in the data center industry is crucial, particularly in relation to existing public cloud providers.
The volatility of AI demand is expected to increase due to consumer market influences, complicating capacity planning for data centers.
The rapid growth in AI technologies necessitates advanced networking infrastructure to support demands such as parallel processing within GPU clusters in data centers.

Key Factors Influencing Power Consumption

Between 2022 and 2030, data centers are projected to account for approximately 0.9 percentage points of the overall 2.4% increase in power demand in the United States, with consumption rising from 3% to 8% of total US power usage by 2030.
Globally, data centers currently represent 1% to 2% of overall energy demand, with predictions indicating this could escalate to as much as 21% by 2030 due to the increasing energy needs related to AI services.
The projected power requirements for large hyperscale data centers are significant, with facilities consuming 100 MW or more, which equates to the annual electricity usage of about 350,000 to 400,000 electric vehicles.
In Europe, power demand could grow by 40% to 50% between 2023 and 2033, driven by both the expansion of data centers and advancements in electrification.
In localized regions, data centers already consume a notable percentage of total electricity, exceeding 10% in at least five US states and over 20% in Ireland.

Hardware and Infrastructure Considerations

AI data centers require advanced hardware, including graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs), to optimize processing for complex computational tasks.
The latest computer chips, such as the Nvidia GB200 NVL72 with the Blackwell B200 GPU, are essential for AI data centers, providing significantly enhanced processing capabilities compared to previous models.
Data centers must be designed for 24/7 operation, ensuring high availability and the ability for super-fast data storage and retrieval to support AI machine learning algorithms effectively.
Networking equipment and software solutions, including AI frameworks and libraries, are crucial components in AI data centers for efficient training, deployment, and management of AI models.
AI data centers are typically subdivided into training and inference facilities, with each type designed to handle different aspects of AI workloads and their unique infrastructure needs.

Cooling and Thermal Management

High water temperature chillers and air-cooling strategies are crucial for maximizing operational productivity and lowering costs in data centers.
The rising demands of AI data centers increase the need for aggressive cooling technologies, vital for maintaining equipment performance in tighter physical spaces.
Liquid cooling solutions, including comprehensive portfolios from CDUs to heat rejection systems, support enhanced thermal management in AI data centers.
Water sourcing for cooling emerges as a critical factor in sustainability assessments for data centers, particularly as equipment density increases.
Implementing effective cooling techniques can potentially reduce global data center electricity demand by 10% to 20%, aligning environmental objectives with financial benefits.

Enhancing Efficiency Through AI Technologies

The efficiency of AI-related computer chips has improved significantly, with modern chips using 99% less power to perform the same calculations as those from 2008, marking a substantial advancement in energy efficiency for AI technologies.
AI innovations can increase computing speed without a proportional rise in electricity consumption, suggesting that some advancements may temporarily enhance efficiency in power usage.
The anticipated increase in data center power consumption from AI is projected to be around 200 terawatt-hours per year between 2023 and 2030, indicating a rising demand for energy as AI becomes more prevalent.
Modern Data Center Infrastructure Management (DCIM) software is being adopted to streamline operations as it replaces manual tracking methods, thus enhancing data center efficiency and management.
As AI models become more efficient and chip production optimization continues, they may be increasingly hardcoded into chips, allowing for greater performance while reducing overall energy consumption in data centers.

Predictive Maintenance

AI data centers require advanced hardware such as graphics processing units, field-programmable gate arrays, and application-specific integrated circuits to optimize processing and handle complex computational tasks.
Solutions for AI-ready data centers include hardware components like AI-optimized servers and storage systems, which are tailored to their specific operational environments.
Effective AI data center design emphasizes the need for high-quality networking equipment and software solutions, including AI frameworks and development tools for efficient model training, deployment, and management.
AI-driven applications are expected to significantly influence the growth of the data center industry, necessitating the development of new strategies tailored to the unique requirements posed by AI.
The increasing demand for power from data centers is projected to see them consume 8% of U.S. power by 2030, illustrating the need for robust infrastructure to support AI workloads.

Resource Optimization

Data centers increasingly utilize smart control systems, such as Data Center Infrastructure Management software, to enhance performance and energy efficiency.
Implementing power capping techniques can limit the amount of power feeding processors and graphics processing units, potentially reducing overall energy consumption by restricting usage to 60% to 80% of total power.
The average power demand of a large data center campus with peak demand of one gigawatt is comparable to the annual energy consumption of about 700,000 households, emphasizing the need for efficient resource management.
Employing more energy-efficient hardware can significantly reduce operating expenses and environmental impact, aligning sustainability with financial goals.
Rethinking AI model training by using less energy-intensive models can contribute to further optimization of resource use in data centers.

Addressing High Energy Costs Associated with AI

The International Energy Agency estimates that global electricity demand from data centers could potentially double between 2022 and 2026, driven significantly by the rising adoption of AI technologies.
Goldman Sachs Research projects that the overall increase in power consumption from AI in data centers could reach around 200 terawatt-hours per year between 2023 and 2030.
A single ChatGPT query consumes approximately 2.9 watt-hours of electricity, in contrast to only 0.3 watt-hours for a Google search, highlighting the intense energy demands of AI applications.
By 2028, AI is expected to account for about 19% of total data center power demand, indicating a substantial rise in energy consumption related to AI workloads.
Analysts anticipate that the expected rise in data center carbon dioxide emissions due to AI will incur a “social cost” estimated between $125-140 billion at present value.

Breakdown of Energy Consumption in Data Centers

The International Energy Agency has projected that global electricity demand from data centers could double between 2022 and 2026, largely driven by the adoption of artificial intelligence.
Goldman Sachs Research estimates that by 2030, data center power demand will grow by 160%, with current consumption levels already accounting for 1-2% of overall power usage.
The energy consumption of data centers is expected to rise to 3-4% of total electricity demand by the end of the decade, leading to a significant increase in carbon dioxide emissions.
A single query processed by AI systems like ChatGPT requires nearly 10 times more electricity compared to a typical Google search, highlighting the stark differences in energy consumption associated with advanced AI processing.
There is a potential risk that data centers could account for up to 20% of global electricity demand by the end of the decade, which could result in strain on local power networks and challenge climate targets.

Strategies for Cost Reduction

Implementing energy-efficient hardware choices can significantly reduce operating expenses in AI data centers, similar to replacing traditional light bulbs with LED bulbs.
Employing power capping techniques can lower energy consumption by limiting processors and graphics processing units to 60% to 80% of their total power capacity.
Simple steps to reduce emissions in AI data centers have the potential to decrease global electricity demand by 10% to 20%, aligning environmental goals with financial incentives.
Investment in sustainability strategies, as demonstrated by companies like Blackstone, can yield substantial energy savings, thus reducing overall operational costs.
Utilizing innovative cooling solutions, such as Green Mountains use of cold fjord water, can lead to significant reductions in cooling costs while enhancing energy efficiency.

Photo by Thomas Richter on Unsplash

Playbook for Reducing Emissions in AI Data Centers

The International Energy Agency has estimated that global electricity demand from data centers could double between 2022 and 2026, primarily driven by the adoption of AI technologies.
AI workloads have significantly increased data center emissions, suggesting a need for effective strategies to reduce energy use and promote sustainable practices in AI data centers.
Short-term energy demands for data centers are primarily sourced from natural gas, which is expected to contribute to the environmental impact if not mitigated with technologies like carbon capture.
The industrys transition towards renewable energy and battery storage solutions is anticipated to help mitigate the current reliance on natural gas for data center operations.
Efficient operational practices and advanced AI-specific hardware and software configurations are crucial for optimizing energy use and reducing emissions in AI data centers.

Sustainable Practices and Innovations

The International Energy Agency estimates that global electricity demand from data centers could double between 2022 and 2026, significantly impacting sustainability efforts as AI adoption increases.
AI workloads have led to soaring data center emissions, highlighting the urgent need for strategies to reduce energy use and promote sustainable AI practices.
By 2028, AI is projected to account for approximately 19% of data center power demand, raising concerns about the associated carbon dioxide emissions and their financial implications.
Industry leaders anticipate substantial investments in renewable energy sources and emerging nuclear generation capabilities to counterbalance the increasing energy demands of AI data centers.
There is a critical need for public-private dialogue among policymakers, tech, and energy sectors to foster responsible AI development and address sustainability challenges in the data center industry.

Leveraging Renewable Energy Sources

Data center developers prefer carbon-free renewable energy but recognize that solar and wind alone may not be sufficient to meet current demands due to their dependence on changing weather conditions.
Major technology companies, including Microsoft, Amazon, and Google, are increasingly investing in nuclear power as a reliable energy source for data centers.
Microsoft is supporting the restart of the Three Mile Island nuclear plant through a power purchase agreement to enhance its energy supply reliability.
The expected rise in data center carbon dioxide emissions from AI innovations indicates a potential social cost of $125-140 billion, highlighting the need for substantial investments in renewable energy and emerging nuclear technologies.
Despite advancements in the efficiency of data centers, the integration of AI applications is anticipated to lead to a significant increase in overall power consumption, making renewable energy sourcing essential for sustainable operations.

Future Trends Shaping AI Data Centers

AI is projected to drive a 165% increase in power demand for data centers by 2030, indicating a significant shift in operational requirements as AI workloads proliferate.
The demand volatility associated with AI applications complicates capacity planning for data centers, as user adoption can escalate rapidly and unexpectedly.
AI-specific data centers necessitate optimized hardware and software configurations tailored for AI workloads to enhance operational efficiency and performance.
The architectural design of AI data centers must incorporate advanced networking equipment and software solutions to support the effective training and deployment of AI models.
Strategic alignment of technology with specific business drivers is vital for successful data center investments, preventing significant financial losses amid the complexities of AI workload management.

Trends in AI Development and Deployment

The evolution of AI models has progressed from static and deterministic methods to fully dynamic models capable of generating probabilistic outputs, facilitated by the declining costs of bandwidth and data storage.
AIs increasing demand is transforming the data center industry, necessitating substantial reconfigurations to manage heightened computing power, energy consumption, and advanced cooling solutions.
Companies must carefully assess data center use cases before construction to ensure alignment with specific business drivers and avoid potential financial losses in an AI-driven future.
Effective AI data center solutions require optimized hardware such as AI-optimized servers and storage systems, as well as high-quality networking equipment, software frameworks, and libraries tailored for machine learning workloads.
The growth of AI may face constraints from the energy sector if generation and grid capacity are lacking in regions where AI data centers are most needed.

Anticipated Advances in Data Center Architecture

AI data centers require advanced hardware components such as graphics processing units, field-programmable gate arrays, and application-specific integrated circuits to optimize processing for complex computational tasks.
The design of AI-specific data centers involves AI-optimized servers and storage systems tailored to their specific operational conditions.
High-performance networking infrastructure is essential for AI workloads, which rely on parallel processing and necessitate robust cabling within GPU clusters and between racks.
Liquid cooling technologies are gaining popularity for efficiently managing heat in data centers that support AI workloads.
Modern Data Center Infrastructure Management (DCIM) software is set to replace manual management tools, streamlining operations and enhancing efficiency in data center management.

Implications for the Energy Sector

Data centers are projected to account for up to 21% of global energy demand by 2030, driven by the increasing energy requirements of AI models and their training processes.
Currently, data centers utilize approximately 1% to 2% of global energy demand, a figure expected to rise significantly due to the soaring demands from AI technologies.
In certain regions, such as Ireland, data centers already represent a substantial portion of electricity consumption, accounting for about one-fifth of the countrys total use.
The International Energy Agency (IEA) acknowledges that while the growth of data centers is noteworthy, it will not be the primary driver of demand in the global electricity market, with other factors like economic growth and electric vehicles being more significant contributors.
The increasing density of data center equipment results in heightened cooling requirements, which often rely on water sources from already stressed watershed areas, presenting challenges for sustainability.
The average size of individual data centers operated by the major tech companies is currently around 40 megawatts, but a growing pipeline of campuses of 250 megawatts or more is coming, according to data from the Boston Consulting Group. Note: CNBC analysis assumes a data center campus is continuously utilizing 85% of its peak demand of a gigawatt throughout the year, for a total consumption of 7.4 billion kilowatt-hours. Source: cnbc.com

Impact on Electricity Demand

Electricity demand from data centers is projected to rise sharply by 2030, with an anticipated increase to 8% of US power usage, up from 3% in 2022.
In large economies such as the United States, China, and the European Union, data centers currently account for approximately 2-4% of total electricity consumption, but their localized impact can be substantial in certain regions.
The International Energy Agency (IEA) estimates that global electricity demand from data centers could double between 2022 and 2026, largely driven by the increased adoption of artificial intelligence.
By 2030, the power needs of data centers in Europe are expected to equal the current total electricity consumption of Portugal, Greece, and the Netherlands combined.
In Ireland, data centers now account for over 20% of the countrys total electricity consumption, highlighting significant regional variances in data center energy usage.
Some estimates suggest that data centers alone could account for 20% of global electricity demand by the end of the decade. Source: forbes.com

Balancing Local and Global Energy Strategies

Intelligent energy-reduction strategies can optimize AI workloads by shifting non-time-sensitive computations to periods of lower energy demand, effectively addressing peak usage and improving energy savings.
Collaboration between institutions, like MIT and Northeastern University, has led to the development of tools that recognize carbon intensity and make adjustments to reduce energy costs by 80% to 90%.
The average data center operated by major tech companies is around 40 megawatts, with upcoming campuses projected to exceed 250 megawatts, indicating a growing trend toward larger facilities to meet energy demands.
Texas has emerged as a prime location for data centers due to its favorable regulatory environment and abundant, tailored energy resources, facilitating innovative power solutions.
With the increasing demand for AI technologies, companies are expanding their data center capacities significantly, with Lancium planning to scale its operations from 1 gigawatt to between three and five gigawatts to accommodate customer needs.

Strategic Investments for Managing Electricity Demand

Investment in new data centers has surged significantly in the past two years, largely driven by the accelerating uptake of artificial intelligence (AI) technologies.
Goldman Sachs Research estimates that the increase in data center power consumption attributed to AI may reach approximately 200 terawatt-hours per year between 2023 and 2030.
By the year 2028, it is expected that AI will account for approximately 19% of the power demand within data centers.
The expected rise in data center carbon dioxide emissions due to increased power consumption could result in a social cost ranging between $125-140 billion at present value.
The surge in electricity demand from data centers is anticipated amidst a historical trend of zero power demand growth in the US, despite increases in population and economic activity.

Investment Opportunities in Energy Infrastructure

Goldman Sachs Research estimates that the overall increase in data center power consumption from AI will reach approximately 200 terawatt-hours per year between 2023 and 2030, leading to significant investment opportunities in energy production and management.
The expected rise in data center carbon dioxide emissions due to increased power demand is projected to represent a social cost of $125-140 billion at present value, highlighting the potential for investments in carbon capture and renewable energy solutions.
Companies like Amazon and Alphabets Google are investing in small nuclear reactors as a means of providing reliable energy supply to data centers, showcasing opportunities in advanced nuclear technology.
The transition towards renewable energy sources for data centers is expected to drive substantial investments, as tech firms aim to reduce reliance on natural gas while enhancing overall energy efficiency in their operations.
As data centers could account for up to 21% of overall global energy demand by 2030 due to AI expansion, there is a growing need for innovative energy solutions and infrastructure that can accommodate this increasing demand sustainably.
In 2023, overall capital investment by Google, Microsoft and Amazon, which are industry leaders in AI adoption and data centre installation, was higher than that of the entire US oil and gas industry – totalling around 0.5% of US GDP. Source: iea.org

Collaborations Between Data Centers and Energy Providers

Data centers, such as those operated by Lancium, must partner with utilities and system operators to ensure they are seen as assets to the grid rather than liabilities, which is crucial for their development and operation.
Developers of large data centers are focusing on carbon-free renewable energy, yet they recognize that solar and wind power alone cannot meet current energy demands due to their variability.
The rapid growth in data centers poses a significant challenge to local power networks, necessitating strategic partnerships to cope with the increasing energy consumption and to avoid raising electricity costs in communities.
Policymakers and regulators require tools and frameworks to understand the evolving demand growth driven by data centers and to address the complexities of scaling energy supply concurrently with data center expansion.
Collaborations between data centers and energy providers are essential to mitigate potential trade-offs in grid reliability and local electricity costs, particularly in regions experiencing a surge in data center construction requests.

What Is an AI Data Center?

An AI data center may refer to either a cutting-edge facility designed to accommodate the intense computational demands of artificial intelligence (AI) workloads or a separate class of facilities that may emerge.

AI workloads consume a lot of data center resources like space, power, cooling, and data/power port connections. Some current data centers can support these high-density workloads, but as GPU-based deployments continue to push rack densities higher, there may be a need for new facility designs.

Characteristics of an AI data center may include:

High-density deployments.AI data centers may need to accommodate power requirements as high as 50 kW per rack.
Innovative cooling solutions.Traditional air cooling is insufficient for the amount of heat generated by AI workloads.Liquid coolingis becoming a more attractive option to efficiently remove heat.
Advanced networking infrastructure.AI workloads heavily rely on parallel processing which demands robust cabling infrastructure within GPU clusters and between racks.
Modern data center management tools.Manual spreadsheets and diagrams will be a thing of the past as data center managers continue to switch to modernData Center Infrastructure Management (DCIM) software.
Streamlined operations and workforce optimization.AI-driven solutions have the potential to address the industry challenge of a skills shortage by automating various tasks.

Source: sunbirddcim.com

At present, data centers worldwide consume 1-2% of overall power, but this percentage will likely rise to 3-4% by the end of the decade. Source: goldmansachs.com

Creating an image with generative AI uses the energy equivalent of fully charging a smartphone Data centers could account for up to 21% of overall global energy demand by 2030 when the cost of delivering AI to customers is factored in. Source: mitsloan.mit.edu

We also expect global electricity consumption from data centres to rise from around 2 per cent of total global electricity demand today, to 9 per cent by 2050. Source: bhp.com