Gartner: Data Centres Face 26% Energy Spike From AI Servers

2026-06-15 · Source: AI Magazine · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Utilities & Infrastructure · Depth: Intermediate, short

Summary

Gartner's latest report forecasts a significant 26% increase in global data center electricity consumption, reaching 565TWh in 2026, up from 447TWh in 2025. This surge is primarily driven by the rapid adoption of AI-optimized servers, which are projected to account for 31% of total data center power consumption in 2026, a rise from 20% in 2025. AI-optimized server consumption climbed over 83% in 2025 and is expected to grow by another 84% in 2026, reaching 175TWh. By 2027, these servers are anticipated to consume 258TWh, surpassing conventional hardware. Cooling infrastructure demand is also set to jump 22.6% to 195TWh in 2026. Gartner estimates total data center electricity demand will hit 290GW and exceed 1,200TWh by 2030, indicating a severe energy deficit as grid capacity struggles to keep pace with AI growth.

Key takeaway

For Directors of AI/ML or IT Professionals planning future infrastructure, you must proactively address power availability as a primary constraint. Prioritize securing long-term power purchase agreements and integrate grid capacity into your site selection criteria. Investing in high-efficiency cooling and edge computing will be crucial to mitigate power limitations and ensure sustainable, scalable AI growth, preventing delays in your AI deployment roadmap.

Key insights

Rapid AI server growth is creating an unprecedented energy deficit, making power availability a critical constraint for data center expansion.

Principles

• Power availability is the new battleground for scaling AI.
• Grid supply will be insufficient for future data center construction.

In practice

• Prioritize efficiency upgrades and secure grid access.
• Invest in high-efficiency cooling systems and edge computing.
• Treat grid capacity as a primary site selection criterion.

Topics

• Data Centers
• AI Servers
• Energy Consumption
• Grid Capacity
• Power Infrastructure
• Cooling Systems

Best for: CTO, VP of Engineering/Data, Executive, AI Architect, Director of AI/ML, IT Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.