📐 Mathematical Formula & Derivation
T_{breakeven} = \frac{\text{CapEx}_{hardware}}{H_{month} \times \left( R_{cloud} - \frac{W_{system}}{1000} \times P_{kWh} \right)} 🔬 Engineering Methodology & Benchmark Analysis
Choosing between cloud GPU instances and on-premise AI workstations depends heavily on utilization factor and local electricity tariffs.
If your team only runs training jobs 3–4 hours per day, on-demand or spot cloud GPUs are vastly cheaper and allow instant scaling to H100s without hardware depreciation.
Conversely, for continuous 24/7 inference pipelines, local workstations often pay for themselves within 7 to 14 months even after accounting for power and cooling.
🛠️ Step-by-Step Engineering Calculation Guide
- Local Hardware Purchase Cost (CapEx): Configure "Local Hardware Purchase Cost (CapEx)" (default benchmark: 4800) or select an industry preset.
- Equivalent Cloud GPU Rental Rate (per Hour): Configure "Equivalent Cloud GPU Rental Rate (per Hour)" (default benchmark: 1.45) or select an industry preset.
- Average Active Compute Hours per Day: Configure "Average Active Compute Hours per Day" (default benchmark: 12) or select an industry preset.
- Total System Power Draw Under Load (Watts): Configure "Total System Power Draw Under Load (Watts)" (default benchmark: 850) or select an industry preset.
- Local Electricity Cost per kWh: Configure "Local Electricity Cost per kWh" (default benchmark: 0.18) or select an industry preset.