By: Tod Higinbotham

Data center operators are facing an infrastructure crisis. According to the International Energy Agency, electricity demand for data centers will more than double by 2030, with AI-specific workloads quadrupling their energy use. AI workloads consume power and generate heat at levels that traditional systems simply cannot handle.   

This isn't just about adding more capacity — it's about fundamentally reimagining how data centers operate. According to a recent report on Scaling AI Infrastructure, Graphics Processing Unit (GPU) clusters now draw tens of megawatts while exhibiting unpredictable power spikes. Individual GPUs can consume more than 1,000 watts compared to traditional Central Processing Units (CPUs) operating at 300 watts, with eight-GPU systems reaching 8,000 watts per tray — representing an 8x increase over traditional configurations. Advanced rack designs are pushing power densities to 600 kilowatts on a pathway to one-megawatt dual-racks. Cooling systems designed for steady-state operations struggle with thermal loads that can overwhelm even well-designed infrastructure.  

Traditional redundancy models, built around predictable workloads, prove inadequate for AI's dynamic demands. AI clusters require a fundamental architectural shift from independent rack operations to serial, interconnected configurations where racks must operate as unified entities with extensive high-speed cabling. This transformation demands designing 'from the chip all the way to the grid' rather than treating infrastructure components as isolated systems. The solution requires integrated innovation across power management, thermal control, and intelligent orchestration systems that can adapt in real-time to workloads that would have been unimaginable five years ago.  

Power and Thermal: The Infrastructure Nexus

AI workloads create a dual challenge: unpredictable power spikes and extreme heat generation. GPU clusters exhibit "pulsed power draw" — consumption that can jump from idle to maximum rapidly — while generating thermal loads that overwhelm traditional air-cooling systems. A single AI training server produces as much heat as several conventional servers, and these aren't temporary spikes, but sustained workload demands.  

Industry research confirms AI's transformative impact: according to the 2025 Data Center Energy Storage Industry Insights Report by Endeavor Intelligence, commissioned by ZincFive, 55% of data center professionals cite increased energy efficiency requirements as AI's biggest effect, while 54% emphasize the growing need for higher power density and smaller footprints.  

Smart Power Management

Solutions like nickel-zinc (NiZn) immediate power battery technologies, for example, provide instant response to power fluctuations, actively smoothing spikes in addition to offering backup power.  This capability proves crucial as AI workloads create synchronized power surges when GPU clusters operate in unison — a phenomenon known as pulse loading. Unlike traditional computing with stable, predictable demands, these millisecond-level fluctuations can overwhelm power systems designed for steady-state operations.  

Immediate Power Solutions (IPS), powered by nickel-zinc (NiZn) batteries, can respond to AI pulse load profiles in milliseconds while also supporting traditional base load demands. This versatility fills a critical gap for operators needing to power both legacy IT infrastructure and next-generation AI workloads within the same facility.  

Provisioning infrastructure for peak pulse loads creates massive inefficiency, with excess capacity idle during off-peak moments.  Nickel-zinc battery chemistry has been proven to respond within milliseconds – enabling data centers to match GPU power profiles in real time and smooth out power spikes, reducing reliance on infrastructure. This approach allows operators to match provisioned capacity to actual workload behavior better, significantly improving both capital and operational efficiency.  

Safety is a growing topic of discussion as data centers scale to support higher power densities, especially for AI. NiZn batteries offer a safer alternative by eliminating thermal runaway at the cell level – a key concern with traditional chemistries – and provide a stable foundation for high-density deployments.  

Rack-level battery integration minimizes distribution losses while providing immediate local response, supporting the broader industry trend toward co-locating generation with demand for grid stability and reduced transmission losses.