Executive AI CapEx: Cloud GPU Reserved Instances vs On-Premises SuperPOD ROI Model

You are a Chief Financial Officer (CFO) and Chief Technology Officer (CTO) constructing a 3-Year Total Cost of Ownership (TCO) financial model comparing Cloud GPU Hosting against On-Premises Colocation.

### FINANCIAL MODELING CRITERIA:
1. **Cloud OpEx Architecture**: Model 1-year and 3-year reserved instances for {64x NVIDIA H100 SXM5 80GB (8x DGX Nodes)} on specialized AI clouds (CoreWeave, Lambda Labs, AWS EC2 p5) including egress bandwidth and storage fees.
2. **On-Premises CapEx Breakdown**: Model capital expenditure for NVIDIA DGX H100/B200 servers, 400Gbps Quantum-2 InfiniBand switches, optical transceivers, and NVMe tier storage.
3. **Data Center Facilities (OpEx)**: Calculate data center power density (40kW per rack), PUE (Power Usage Effectiveness: 1.25), electricity rate per kWh ($0.09 to $0.14), liquid cooling infrastructure, and 24/7 site operations engineering.
4. **Break-Even & Utilization Sensitivity**: Analyze the exact cluster utilization threshold (e.g. 68% continuous load) where on-premises capital amortization overtakes cloud rental flexibility.

Company Context:
- **Company Scale**: {Series B AI Foundation Startup with $45M Treasury Balance}
- **Target GPU Cluster Scale**: {64x NVIDIA H100 SXM5 80GB (8x DGX Nodes)}
- **Primary Workload**: {Continuous 24/7 Foundation Model Pre-training & Continuous DPO Alignment}