Program pillars
Site Selection & Incentives
Grid proximity/substation capacity, fiber routes, zoning/permitting, federal/state incentives, water strategy, seismic/flood/fire risk. Choose the right location before breaking ground.
Power & Cooling
MW-scale planning, UPS/generator strategy, hot/cold aisle or containment, liquid/immersion options, PUE/WUE targets, DCIM telemetry. Keep GPUs fed and cool without waste.
GPU Cluster Architecture
Training vs inference topologies, fabric networking (e.g., high-bandwidth Ethernet/IB), storage tiers (NVMe/HDD/object), orchestration (Kubernetes/Slurm), scheduling, observability. Build clusters that train faster and serve reliably.
FinOps & Budgets
CapEx vs OpEx models, TCO/ROI, capacity roadmaps, supply chain risk, staged buildouts, cloud vs colo vs greenfield decisioning. Defend your budget to the board.
Sustainability & Compliance
Carbon-aware scheduling, heat reuse, environmental reporting, physical security & OT network segmentation, safety standards. Meet ESG goals without compromising performance.
Colocation vs. Greenfield: Which path?
Colocation
Faster time-to-market, shared infrastructure, lower upfront CapEx
Speed: 3-6 months to deployment
Cost: Lower initial investment
Flexibility: Limited customization
Best for: Proof of concept, rapid scaling
Greenfield
Full control, optimized for your workload, long-term cost efficiency
Speed: 18-36 months to commissioning
Cost: Higher CapEx, lower OpEx over time
Flexibility: Full architectural control
Best for: Strategic infrastructure, large scale
Metrics that matter
PUE ≤ 1.3
Power Usage Effectiveness target for AI workloads
99.99%
Uptime SLA for training clusters
400Gbps+
Fabric bandwidth per GPU node