Data Center Power Capacity: What You Need to Know

Power capacity is the single most important specification when evaluating a data center — and with AI workloads consuming 5-10x more power per rack than traditional enterprise computing, understanding power capacity has never been more critical. This guide covers everything you need to know about data center power in 2026, from megawatt ratings and power density to redundancy architectures and the grid constraints that are reshaping the industry.

Understanding Megawatt (MW) Ratings

When a data center advertises "50 MW of capacity," what does that actually mean? The megawatt rating refers to the total critical IT load the facility can support — the amount of power available to run servers, storage, and networking equipment after accounting for cooling, lighting, and other overhead.

However, there's an important distinction between several power metrics:

• Total utility feed: The raw power coming from the utility grid. A 50 MW IT load facility might have 70-80 MW of total utility power to account for cooling and infrastructure overhead.
• Critical IT load: The power available for actual computing equipment. This is the number that matters most when sizing your deployment.
• Contracted power: The amount of power a tenant has committed to in their lease agreement. Facilities typically sell power in increments of kW or MW.
• Peak vs sustained: Some facilities quote peak capacity while others quote sustained capacity. AI training workloads run at near-peak continuously, so sustained capacity is the more relevant metric.

Power Density: kW Per Rack

Power density — measured in kilowatts (kW) per rack — describes how much power is available to each server cabinet. This metric has changed dramatically with AI:

• Traditional enterprise (5-8 kW/rack): Standard servers, storage arrays, and network equipment. Most older data centers were designed for this range.
• High-performance computing (15-20 kW/rack): Dense compute clusters, financial trading systems, and early GPU deployments. Requires enhanced cooling but can work with standard air cooling in many cases.
• AI training (40-80 kW/rack): NVIDIA DGX systems, large GPU clusters, and AI training infrastructure. An 8-GPU H100 server draws approximately 10 kW, and a full rack can exceed 60 kW. This level requires liquid cooling or advanced air cooling solutions.
• Next-gen AI (100-150+ kW/rack): NVIDIA GB200 NVL72 racks are expected to draw 120+ kW per rack. Only purpose-built facilities with liquid cooling can support these densities.

Power Redundancy Architecture

Data center power systems are designed with multiple layers of redundancy to prevent downtime. Understanding the redundancy architecture is critical when evaluating facilities:

N+1 Redundancy

The baseline for any quality data center. N+1 means there is one additional component beyond what's needed for the full load. For example, if 4 UPS modules are needed to support the load, an N+1 system has 5 modules. If any single component fails, the remaining components can handle the full load. This provides protection against single points of failure but not against maintenance windows.

2N Redundancy

Full redundancy — two completely independent power paths, each capable of supporting the entire load. If the entire A-side power system fails, the B-side takes over with no interruption. This is the standard for Tier III and Tier IV facilities and is what most enterprise and AI workloads require. The cost premium over N+1 is typically 30-50%, but the reliability improvement is dramatic.

2N+1 Redundancy

The gold standard — two independent power paths plus additional spare capacity. This allows for simultaneous maintenance on one path while still maintaining redundancy on the other. Only the most critical facilities (financial trading, government, military) typically invest in 2N+1.

UPS Systems and Battery Storage

Uninterruptible Power Supply (UPS) systems bridge the gap between a utility outage and generator startup (typically 10-30 seconds). Modern data centers use several UPS technologies:

• Traditional lead-acid UPS: Reliable and well-understood, but large, heavy, and requires regular battery replacement every 3-5 years.
• Lithium-ion UPS: Increasingly common, offering 2-3x the lifespan of lead-acid, smaller footprint, and better energy density. Higher upfront cost but lower total cost of ownership.
• Rotary UPS (flywheel): Uses kinetic energy stored in a spinning flywheel. No batteries to replace, very high power density, but limited runtime (typically 15-30 seconds — enough to bridge to generators).
• Battery Energy Storage Systems (BESS): Some modern facilities integrate large-scale battery storage that can provide extended runtime and even participate in grid services, offsetting costs.

Generator Systems

Diesel generators are the backbone of data center backup power, providing extended runtime when utility power is unavailable. Key considerations include:

• Fuel storage: Most facilities maintain 24-72 hours of on-site diesel storage, with contracts for emergency fuel delivery. AI training runs that take weeks or months need facilities with robust fuel logistics.
• Tier rating: Generator capacity directly impacts the facility's tier rating. Tier III requires N+1 generator redundancy; Tier IV requires 2N.
• Testing regime: Quality operators test generators weekly under load and perform annual full-transfer tests. Ask about testing practices during your evaluation.
• Environmental regulations: Diesel generator emissions are increasingly regulated, particularly in California and the Northeast. Some jurisdictions limit annual runtime hours, which can affect backup power availability.

How AI Is Changing Power Requirements

The AI revolution is fundamentally reshaping data center power requirements in several ways:

Massive Power Increases

A single NVIDIA DGX H100 system draws approximately 10 kW. A rack of 4 DGX systems draws 40+ kW. A 1,000-GPU training cluster can require 1-2 MW of power — more than many small data centers can provide in total. The next generation of GPU systems (B200, GB200) will push power requirements even higher.

Constant Load Profiles

Unlike traditional enterprise workloads that have peaks and valleys throughout the day, AI training runs at maximum power 24/7 for weeks or months. This eliminates the traditional headroom that data centers relied on between peak and average utilization, requiring more conservative power provisioning.

Grid Strain

The explosive growth of AI data centers is straining electrical grids across the US. Northern Virginia, the world's largest data center market, is facing multi-year delays for new utility connections. Texas, Georgia, and other markets are experiencing similar challenges. Some key data points:

• US data center power demand is projected to reach 35 GW by 2030, up from approximately 17 GW in 2024
• AI workloads account for an estimated 40% of new data center power demand
• Utility connection timelines have stretched from 12-18 months to 3-5 years in some markets
• Several major markets are implementing moratoriums or restrictions on new data center connections

Power Pricing Models

Understanding how data centers charge for power is essential for budgeting:

• Per-kW pricing: The most common model. You pay a monthly rate per kW of committed power (typically $100-200/kW/month in major markets). This rate covers the power infrastructure — transformers, UPS, generators, distribution — but usually not the electricity itself.
• Metered power: You pay for actual electricity consumed, typically at a markup over the utility rate. This is in addition to the infrastructure charge.
• All-inclusive pricing: Some providers bundle power infrastructure and electricity into a single per-kW rate, simplifying budgeting but sometimes at a premium.
• Power Usage Effectiveness (PUE) impact: A facility's PUE directly affects your power costs. A PUE of 1.3 means you pay 30% more for electricity than your IT equipment actually consumes (the 30% goes to cooling and overhead). Top-tier facilities achieve PUE of 1.1-1.2.

Evaluating Power Capacity for AI Deployments

When evaluating a data center for AI workloads, ask these critical power questions:

• What is the maximum power density per rack? Ensure the facility can support your target density (30-80+ kW for AI).
• What is the total available power, and how much is committed? A 50 MW facility that's 95% sold has very different expansion potential than one at 60%.
• What is the utility connection timeline for additional power? In some markets, this can be years.
• What cooling technology supports the high-density zones? Air cooling alone cannot efficiently handle 40+ kW per rack.
• What is the power redundancy architecture? 2N is the minimum for production AI training clusters.
• What is the PUE, and does it change at high density? Some facilities achieve excellent PUE at standard density but struggle at AI density levels.

The Future of Data Center Power

Several trends are shaping the future of data center power:

• On-site generation: Some operators are exploring on-site natural gas or even small modular nuclear reactors (SMRs) to bypass grid constraints
• Direct utility partnerships: Large operators are signing long-term power purchase agreements directly with utilities and renewable energy developers
• Grid-interactive data centers: Facilities that can modulate their power consumption to support grid stability, potentially generating revenue from demand response
• 48V DC power distribution: Google and others are pioneering 48V DC distribution within the data center, reducing conversion losses and improving efficiency

Explore facilities by power capacity in our directory, or learn about cooling technologies that enable high-power-density deployments.