Energy-efficient HPC data center strategies for 2025
- Cedric KTORZA
- Oct 7
- 7 min read
Datacenter calcul haute performance must become radically more energy‑efficient in 2025. This article gives you a practical, end‑to‑end playbook to design, retrofit, and operate HPC data center infrastructure that cuts energy use, unlocks density for AI/ML workloads, and reduces carbon without compromising performance.
In brief
Focus on high-impact levers: advanced liquid cooling, right‑sized power trains, and energy‑aware scheduling.
Track metrics beyond PUE: include WUE, CUE and job‑level energy per simulation/training run.
Align compute with green power windows and integrate on‑site renewables plus storage.
Start with telemetry and airflow containment quick wins; plan liquid cooling and modular UPS in phases.
Partner with an integrator: at Score Group, Noor ITS, Noor Energy and Noor Technology coordinate facility, IT and AI/IoT layers.
Lever | What it changes | Typical impact (order of magnitude) |
Liquid cooling (direct‑to‑chip/immersion) | Removes heat efficiently from GPUs/CPUs | 20–40% cooling energy reduction; enables 50–100 kW/rack densities |
Airflow containment + economization | Cuts mixing losses, leverages ambient cooling | 5–15% facility energy savings where climate allows |
UPS modernization (eco‑mode, Li‑ion) | Lowers conversion losses, improves lifecycle | 2–5% facility energy; higher availability |
Energy‑aware scheduling | Shifts and caps workloads for grid/carbon | 5–20% IT energy per job; lower carbon intensity |
On‑site PV + storage | Offsets grid use; peak shaving | Variable by site; improves resilience and carbon |
Why energy efficiency is critical for HPC in 2025
The HPC energy challenge: AI, simulation and density
AI training clusters and classic HPC simulations are pushing rack densities from 30–50 kW toward 80–120 kW and beyond. Without a rethink, power and cooling overheads escalate, stranding capacity and budget. The IEA notes that data center electricity demand is rising fast as AI adoption grows, underscoring the urgency to optimize both facility and IT layers in tandem. See the IEA’s recent analysis of data centres and AI for context on growth and electricity trends in 2023–2024.
Efficiency is not a single project—it’s a continuous discipline across design, operations, and scheduling.
Metrics that matter (beyond PUE)
PUE (Power Usage Effectiveness): Aim for sustained, transparent measurement; mature sites often target ≤1.2 under steady load.
WUE (Water Usage Effectiveness): Track water impacts of evaporative systems; consider non‑potable sources and adiabatic controls.
CUE (Carbon Usage Effectiveness): Use grid carbon data to report CO₂e per kWh; connect with energy‑aware job scheduling.
Job‑level energy: kWh per training run or per simulation provides the most useful efficiency signal for HPC users.
Thermal compliance: Follow ASHRAE TC9.9 classes for safe operating ranges when warming setpoints to reduce cooling energy.
Reference: ASHRAE’s guidance for data center thermal management and classes provides operating envelopes and best practices. Uptime Institute’s 2024 survey also highlights persistent PUE plateaus and the need for systemic changes.
External sources:
ASHRAE – Data center technical resources
Uptime Institute – Global Data Center Survey
Green500 – Energy-efficient supercomputers
Facility strategies that move the needle
Right‑size and modernize the power train
Use modular, high‑efficiency UPS with eco‑mode and high part‑load efficiency; consider lithium‑ion batteries or flywheels for better round‑trip performance and footprint.
Optimize distribution: 400/230V (or 415/240V) to reduce conversion steps; busway distribution enables incremental capacity and lower losses.
Implement granular power metering (per breaker/row/rack) with an Energy Management System (EMS) to surface drift and ghost loads.
Design for concurrent maintainability: keep efficiency gains without sacrificing availability or SLAs.
At Score Group, our Noor ITS division designs and optimizes the electrical backbone, while Noor Energy integrates metering and EMS to keep the power chain efficient and observable.
Advanced cooling for high‑density racks
Direct‑to‑chip liquid cooling: Moves heat at high ΔT with warm water, often enabling chiller‑less operation for much of the year.
Rear door heat exchangers: A pragmatic bridge for air‑cooled servers, reducing hot‑aisle temperatures and fan power.
Two‑phase immersion: Maximizes heat transfer and density; consider operational workflows, fluid handling, and vendor lock‑in.
Warm‑water loops (30–45°C): Expand free‑cooling windows and enable heat reuse to buildings or process loads where feasible.
Noor ITS designs the mechanical plant; Noor Energy handles heat‑recovery integration and Building Management System (BMS) controls to keep setpoints within ASHRAE guidance while minimizing energy.
Containment, airflow, and economization
Full hot‑aisle (or cold‑aisle) containment prevents mixing, reducing fan speeds and coil loads.
Intelligent economization: Indirect evaporative cooling where climate allows; pair with adiabatic controls to safeguard WUE.
Rack hygiene: Blanking panels, brush strips, cable management and pressure‑controlled fans can deliver immediate savings.
Continuous optimization through CFD‑backed moves/adds/changes and rule‑based alarms for drift.
IT-centric levers for HPC efficiency
Energy‑aware scheduling and power capping
Cluster schedulers (e.g., Slurm) can apply power caps per node/GPU and pack jobs to maximize utilization of the most efficient bins.
Carbon‑aware scheduling: Shift flexible batch workloads into low‑carbon grid windows; surface projected CO₂e per job in the queue.
Dynamic Voltage and Frequency Scaling (DVFS) and application‑level throttling: Identify plateaus where performance barely changes while power drops.
Telemetry loops: Feed node‑level power, inlet temperature, and performance counters into policies that auto‑tune limits.
Noor Technology integrates AI/analytics to correlate job telemetry with facility data, while Noor ITS ensures secure connectivity and data pipelines from DCIM/EMS to the scheduler.
Firmware, bins, and silicon choices
Choose accelerators and CPUs with high performance per watt for your workloads; verify with vendor‑neutral benchmarks.
Leverage GPU partitioning (e.g., MIG) to raise effective utilization for smaller jobs, avoiding idle silicon.
Keep firmware and drivers current; energy optimizations often arrive via microcode and runtime libraries.
Track external benchmarks such as the Green500 to calibrate efficiency targets for similar architectures.
Data management and storage tiers
Use high‑IOPS NVMe tiers for hot data and parallel file systems; migrate cold datasets to object storage with lifecycle rules.
Compress checkpoints and logs; reduce write amplification and network chatter.
Schedule data movement during green power windows; pre‑fetch to align with training or simulation runs.
De‑duplicate reference datasets across teams to avoid wasteful replication.
Grid and sustainability integration
On‑site renewables and storage
Rooftop/ground‑mount PV and battery energy storage can shave peaks and support micro‑outages.
Evaluate thermal storage (e.g., chilled water) and heat reuse to nearby facilities to improve total energy productivity.
Contractual instruments (PPAs, GoOs/RECs) can complement physical assets for 24/7 carbon‑aware operations.
Noor Energy covers feasibility, engineering, and integration into EMS/BMS, ensuring generation and storage coordinate with compute schedules.
Demand response and flexibility
Classify workloads by flexibility: urgent, shiftable (hours), deferrable (days). Bind policies in the scheduler.
Expose grid signals (price, CO₂ intensity, DR events) to your orchestration layer to modulate job starts, power caps, or cooldowns.
Quantify impact: report avoided CO₂e and energy cost, along with any impact on queue wait times and job SLAs.
Design and operations blueprint with Score Group
Score Group acts as an integrator across three pillars—Energy, Digital, and New Tech—to deliver measurable efficiency:
Assess
Noor ITS: Site audit, electrical/mechanical baseline, DCIM/telemetry readiness.
Noor Energy: EMS maturity, utility data, PUE/WUE/CUE instrumentation.
Noor Technology: Data ingestion from IT and facility, AI anomaly detection potential.
Architect
Cooling strategy (rear door vs direct‑to‑chip vs immersion) aligned to rack roadmaps.
Power train modernization, scalable busways, selective redundancy.
Data model: unify DCIM + EMS + scheduler telemetry for consistent KPIs.
Implement
Containment and economization upgrades first; then liquid cooling enablement in phases.
UPS modules, Li‑ion retrofit, granular metering.
Scheduler policies for power capping, carbon windows, and job placement.
Operate and improve
Closed‑loop controls: temperature setpoints, fan curves, and workload shifts.
Quarterly tuning with CFD refresh and firmware updates.
Transparent reporting to stakeholders on energy, carbon, and job‑level efficiency.
Explore our approach and get in touch via the Score Group website.
A pragmatic 24‑month roadmap
0–90 days: instrument and optimize
Enable per‑rack power and temperature telemetry; fix airflow leaks, add blanking and grommets.
Implement hot/cold aisle containment; raise supply air temperature within ASHRAE limits.
Activate UPS eco‑mode where compatible; update firmware and power policies.
Start reporting kWh per job; publish a simple monthly PUE/WUE snapshot.
3–12 months: scale cooling and power levers
Install rear door heat exchangers for hottest rows; plan direct‑to‑chip for next GPU generation.
Retrofit modular UPS with Li‑ion; deploy busway with metered tap‑offs.
Integrate EMS/DCIM with the scheduler; pilot carbon‑aware job shifting.
12–24 months: transform and automate
Commission direct‑to‑chip (or immersion) liquid cooling islands for 80–120 kW/rack.
Add on‑site PV and battery storage where viable; participate in DR programs.
Automate closed‑loop controls for setpoints and scheduling based on grid/carbon signals.
Institutionalize quarterly efficiency sprints with clear, job‑level KPIs.
FAQ
How do I choose between rear‑door, direct‑to‑chip, and immersion cooling for HPC?
Start with heat density, hardware roadmap, and operational constraints. Rear‑door heat exchangers are fast to deploy and suit mixed racks up to tens of kW. Direct‑to‑chip liquid cooling fits GPU/CPU nodes at 50–120 kW/rack with warm‑water loops and broad vendor support. Two‑phase immersion delivers maximum density and acoustic/airflow simplicity but requires fluid handling changes and careful vendor selection. Model total cost of ownership and operational impacts; many sites run hybrid environments during transition phases.
What PUE target is realistic for a modern HPC facility?
Targets depend on climate, redundancy, and density. Well‑optimized sites with containment, economization, and partial liquid cooling often sustain PUE in the 1.15–1.25 range at steady load. However, PUE alone is insufficient—pair it with WUE, CUE, and job‑level kWh per training run or simulation. Focus on stable, transparent measurement across seasons and upgrades rather than chasing a single headline value that may not reflect workload reality.
How can schedulers reduce energy without hurting performance?
Use power capping and DVFS where performance plateaus exist; many HPC and AI workloads retain near‑peak throughput at slightly reduced clocks. Pack jobs to keep efficient nodes busy and idle the least efficient ones. Shift flexible jobs into low‑carbon or low‑price grid windows. Expose power and carbon metrics in the queue so users can opt in to greener slots. Measure the delta: report energy per job before/after policies and adjust caps to respect SLAs.
Does liquid cooling always beat air on efficiency?
For high‑density racks and accelerators, liquid usually wins because it removes heat with higher ΔT and less fan work, enabling warmer setpoints and more free‑cooling hours. That said, the best solution is contextual. If your racks are ≤20–30 kW, excellent containment plus economization may rival liquid for net facility efficiency. Consider lifecycle, serviceability, and roadmap: many operators start with rear‑door cooling and evolve to direct‑to‑chip as densities rise.
What KPIs should I report to leadership?
Report a concise set: monthly PUE/WUE/CUE with context; total kWh and CO₂e; average and 95th percentile inlet temperatures; IT utilization; and job‑level kWh per training run or simulation. Add project‑specific KPIs (e.g., cooling energy share, UPS losses, DR participation). Tie these to business outcomes—more jobs per MWh, fewer thermal incidents, shorter provisioning lead times—so efficiency is seen as an enabler, not a constraint.
Key takeaways
Prioritize liquid cooling, containment, and UPS modernization to unlock density and reduce overheads.
Measure what matters: combine PUE/WUE/CUE with job‑level energy per workload.
Align compute with cleaner power through carbon‑aware scheduling and on‑site generation/storage.
Instrument first; then phase upgrades to avoid stranded capacity and downtime.
Partner with an integrator: at Score Group, Noor ITS, Noor Energy, and Noor Technology orchestrate the facility, IT and New Tech stack end‑to‑end.
Ready to accelerate your HPC efficiency journey? Visit Score Group to connect with our experts.
