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name: data-center-compute-supply-efficiency description: | Guides data center compute supply and resource efficiency—capacity and utilization planning, kW-per-useful-compute metrics, stranded power and rack space, hardware refresh and decommission, power-aware placement, consolidation and right-sizing of physical hosts, GPU/CPU supply alignment with workload demand, and sustainability reporting (PUE, carbon intensity). Use when optimizing on-prem or colo compute footprint, forecasting server/GPU supply, reducing idle capacity, improving DC utilization, or balancing efficiency vs performance SLAs—not for greenfield facility design (data-center-design-execution-lead), K8s deploy/troubleshoot (cluster-deployment-engineer), cloud-only FinOps (infrastructure-engineer scope varies), or LLM token cost programs (ai-token-improvement-plan-engineer), or multi-site investment prioritization and steering (data-center-portfolio-planning-execution-lead), or MW/rack construction delivery (senior-data-center-capacity-delivery-manager). Compute accounting: compute-accounting-manager.

Data Center Engineer — Resource Efficiency (Compute Supply)

When to Use

• Measure and improve utilization of racks, kW, and compute (CPU/GPU/memory)
• Forecast compute supply: how many nodes/GPUs needed by quarter
• Find stranded capacity (power allocated but unused, empty U, low CPU%)
• Plan hardware refresh, standard builds, and end-of-life decommission
• Consolidate workloads to free racks or defer capex
• Set power caps and placement rules for efficiency without breaching SLAs
• Report efficiency KPIs to finance, sustainability, and engineering leadership
• Compare efficiency of keeping workloads on-prem vs shifting burst to cloud

When NOT to Use

• New hall design, MEP, commissioning → data-center-design-execution-lead
• Helm, cluster upgrades, pod debug → cluster-deployment-engineer
• VPC, Terraform, managed cloud architecture → infrastructure-engineer
• AI inference token/cost roadmap → ai-token-improvement-plan-engineer
• AI production ops cadence → ai-lead-ops
• Multi-vendor DC construction program → technical-program-manager
• Multi-site DC roadmap and capex prioritization → data-center-portfolio-planning-execution-lead

Related skills

Need	Skill
Facility design and build	data-center-design-execution-lead
K8s scheduling and workloads on clusters	cluster-deployment-engineer
Hybrid cloud and virtualization	infrastructure-engineer
Large efficiency program coordination	technical-program-manager
Enterprise DC portfolio and steering	data-center-portfolio-planning-execution-lead
Rack-ready / MW delivery execution	senior-data-center-capacity-delivery-manager
Server/GPU sourcing and supplier SCM	supply-chain-manager
On-site install, asset/serial capture	field-services-engineer
Executive/sustainability messaging	communication-lead
Compliance evidence for facilities	compliance-engineer
Compute capex, depreciation, cloud GL	compute-accounting-manager
RL training GPU utilization patterns	ml-systems-engineer-rl-engineering

Core Workflows

1. Baseline efficiency metrics

Establish dashboards for:

• Facility: PUE, total IT kW, cooling kW
• Supply: rack count, kW committed vs kW used, GPU/CPU inventory
• Demand: avg/peak utilization, useful work per kW (define numerator per org)
• Waste: idle hosts, powered empty U, oversubscribed cooling margin

See references/efficiency_metrics.md.

2. Compute supply planning

1. Demand — workload growth, new products, GPU training vs inference mix
2. Supply — on-hand, on-order, lead times, standard SKUs
3. Gap — quarter-by-quarter surplus or deficit
4. Actions — buy, refresh, cloud burst, or defer

See references/compute_capacity_supply.md.

3. Utilization and consolidation

• Inventory hosts below utilization threshold for 30+ days
• Plan migration windows; validate performance tests post-move
• Target: raise average utilization without violating HA or latency SLOs
• Virtualization or K8s density changes → coordinate with cluster-deployment-engineer

See references/utilization_optimization.md.

4. Power and thermal efficiency

• Align rack kW nameplate with actual draw; recover stranded breaker capacity
• Power capping policies (OS/firmware/IPMI) where SLA allows
• Match GPU trays to cooling class (air vs liquid)

See references/power_thermal_management.md.

5. Hardware lifecycle

Stage	Efficiency focus
Standardize	Few SKUs → spare pool efficiency
Deploy	Fill racks to target kW; avoid one-off configs
Operate	Monitor age, warranty, power draw drift
Refresh	TCO: new gen perf per watt vs extend
Decommission	Power down, wipe, reclaim U and kW

See references/hardware_lifecycle.md.

6. Reporting and targets

• Monthly: utilization, PUE trend, supply vs demand
• Quarterly: refresh plan, capex avoidance from consolidation
• Tie narratives to sustainability goals without greenwashing

See references/reporting_targets.md.

Output standards

• Supply/demand table by quarter (nodes, kW, GPUs)
• Top 10 stranded assets with recommended action and risk
• Efficiency initiative backlog with estimated kW or capex saved
• Assumptions explicit (utilization window, SLA exclusions)

When to load references

• KPIs and formulas → references/efficiency_metrics.md
• Forecast and procurement → references/compute_capacity_supply.md
• Consolidation and right-size → references/utilization_optimization.md
• Power caps and cooling fit → references/power_thermal_management.md
• Refresh and decomm → references/hardware_lifecycle.md
• Dashboards and targets → references/reporting_targets.md