Reducing Data Center Energy Consumption: Challenges & Solutions

AI Boom → Soaring Electricity Demand

The AI wave is colliding with the power grid. The rapid rise of AI – especially GPU-heavy model training and inference – has triggered a surge in data-center power consumption, with significant economic and environmental implications.

In the U.S., wholesale electricity prices are spiking near AI data-center hubs. Grid operators are reworking their demand forecasts. Utilities are shifting capital toward meeting data-center load rather than renewable interconnections. By 2030, U.S. data centers could account for as much as 12% of national electricity consumption.

The trend is global. BloombergNEF forecasts that average hourly demand from data centers will nearly triple in the U.S. by 2035. Globally, the sector could exceed 4% of all electricity use.

Who’s Feeling the Strain (And Why It Matters)

Even the biggest hyperscalers are feeling the energy pinch:

• OpenAI, in partnership with Microsoft, is planning one of the most ambitious AI data-center expansions globally. Its “Stargate” initiative – potentially requiring up to 10 gigawatts of power – could match the energy needs of a small country. The first phase alone may cost $100 billion, with buildouts planned across the U.S. The scale of this project reflects not only the rising compute demand of frontier models, but also the unprecedented infrastructure and grid pressure that comes with it.
• Microsoft reported a 23% increase in total emissions versus 2020, attributing the rise to AI and cloud growth. While committing to 100% zero-carbon electricity by 2030, the company is investing in aggressive efficiency improvements.
• Google saw its overall emissions rise ~51% versus 2019, citing the scale of AI compute. Despite signing 8 GW of clean power deals, the company is racing to offset its soaring demand.
• Amazon (AWS) noted emissions increased for the first time in three years, driven by datacenter and fleet expansion. AWS’s next-gen cooling architecture claims up to 46% lower mechanical energy at peak.
• Meta recently announced a new AI data center in El Paso – designed to scale to 1 gigawatt of capacity – illustrating the scale frontier AI now demands.

Cost, Grid & Community Impacts

Price Pressure & Infrastructure Stress

• In the U.S., power bills are rising in regions like the PJM Interconnection (Mid-Atlantic) as data center developers reserve massive capacity, even before sites go live.
• Utilities are prioritizing data-center-related grid upgrades over renewable interconnections, risking delays to decarbonization plans.

Regulatory Headwinds in the EU

• The EU is moving forward with data center energy disclosure requirements and considering minimum efficiency standards.
• Germany has enacted binding PUE requirements, raising the bar for new and existing sites – particularly GPU-intensive AI deployments.

What’s Working: Practical Levers to Cut Energy Use

Facility-Side: Infrastructure Optimization

• Liquid Cooling: As power density rises with GPU clusters, switching to liquid cooling is increasingly essential. Well-designed AI halls using liquid cooling can now achieve PUE levels between 1.2–1.3, dramatically improving energy efficiency and reducing overhead from heat management.
• Smarter Site Selection: Locate data centers in areas with reliable, low-carbon electricity and spare capacity. Grid-ready infrastructure ensures scalability without grid strain or carbon-intensive stopgaps.
• Real-Time Monitoring: Implement advanced telemetry for energy, thermal, and water usage to gain granular control and ensure compliance with emerging EU and U.S. disclosure mandates.

Compute-Side: Do More Work per Watt

Massive Savings for Big Data Workloads
Enterprises processing large-scale, high-frequency data – from manufacturing telemetry to behavioral analytics – can slash energy costs by up to 70% simply by migrating from traditional CPU-based analytics stacks to native GPU database technology like SQream.

In one real-world deployment from a chip manufacturing customer, SQream reduced 250 CPU-based servers to just 12 GPU compute machines and 13 storage nodes.

The results:

• >66% lower energy consumption
• <1/3 the kilowatt-hours per job
• 90% fewer racks
• Up to 10x more cost-effective at scale

Fully Utilize GPUs: SQream’s orchestration ensures GPUs stay busy – minimizing idle time and shortening job durations. This leads directly to lower energy per query and more predictable performance at scale.

Minimize Data Movement: By consolidating analytics, ETL, and querying inside a single GPU environment, SQream reduces energy-wasting data shuffles between services or silos – which also lightens the load on cooling systems.

Why GPUs Are (Still) Part of the Solution

While GPUs are power-intensive per unit, they deliver significantly higher throughput per watt than general-purpose CPUs when properly utilized.

That means:

• Fewer machines
• Shorter runtimes
• Smaller physical and carbon footprint

The equation is simple: more insight per kilowatt-hour.

How SQream Helps Reduce Energy Use

SQream’s GPU-native data preparation, processing, and analytics engine offers a direct response to these energy and infrastructure challenges.

• Shrink the Hardware Footprint
  By consolidating CPU-bound data analytics onto GPU servers, SQream enables organizations to do more with fewer machines – cutting IT power consumption and reducing associated cooling needs.

• Maximize Compute-Per-Watt
  SQream’s parallel processing architecture finishes more queries per joule by distributing SQL, Python, ETL, and ML workloads across GPU cores. This slashes power per job and enables faster, deeper analytics without bloated infrastructure.
• Reduce Cooling and Overhead
  Fewer servers, shorter jobs, and tighter orchestration all contribute to lower thermal output and smoother electrical provisioning. This improves effective PUE and eases strain on UPS, gensets, and HVAC systems.
• Accelerate the Entire Data Lifecycle
  Unlike siloed analytics stacks, SQream manages the full data journey – from ingestion and transformation to querying, ML training, and reporting – within a single GPU-accelerated environment. That means fewer moving parts, less wasted energy, and higher ROI on every kilowatt.

The Outcome

For data leaders and infrastructure operators, SQream delivers a way to scale AI-era analytics while actively reducing power consumption and cost exposure.

Benefits include:
– Lower energy bills
– Fewer servers and racks
– Smaller data center footprints
– Easier compliance with EU and U.S. efficiency directives
– Greater environmental sustainability without performance trade-offs

Last Thoughts

The electricity bill for AI is arriving – and it’s steep.

As power-hungry AI workloads scale, operators can no longer rely on infrastructure alone to deliver efficiency. Leaders like Google, Microsoft, Amazon, and Meta are racing to balance performance, cost, and sustainability  – and it’s clear that the answer lies not just in greener facilities, but in smarter compute.

SQream offers a path forward: GPU-accelerated analytics that reduce energy usage, minimize hardware sprawl, and deliver real-time insight  – all while shrinking your carbon and financial footprint.

For organizations serious about scaling AI while staying within energy and emissions budgets, SQream is more than a database – it’s an energy strategy.