AI GPU Compute Energy Cost Estimator
Calculate the electricity costs of running AI inference and training clusters. Compare Blackwell vs Hopper GPU power efficiency for 2026 scale.
Total Cluster Power (kWh/hr)
Total Daily Energy Cost ($)
Strategic Optimization
AI GPU Compute Energy Cost Estimator
In the era of trillion-parameter models, electricity is the primary bottleneck for AI throughput. This calculator provides a professional model for projecting the OpEx of high-density AI clusters based on current 2026 energy rates and hardware specifications.
Hardware Efficiency Metrics
- Hopper (H200): Standard performance-per-watt benchmark for 2025.
- Blackwell (B200): Next-gen 2026 efficiency, often requiring liquid cooling for maximum density.
- PUE (Power Usage Effectiveness): Data center overhead. A PUE of 1.1 is the global elite standard, while 1.5 is typical for legacy air-cooled facilities.
Strategic Insight: The Cooling Factor
As TDP (Thermal Design Power) per chip crosses 1000W, the choice of cooling (Direct-to-Chip Liquid vs. Immersion) becomes a critical variable in your cost-per-inference. Liquid-cooled clusters often see a 20-30% reduction in total energy OpEx compared to traditional air-cooled setups.
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Disclaimer
This calculator is provided for educational and informational purposes only. It does not constitute professional legal, financial, medical, or engineering advice. While we strive for accuracy, results are estimates based on the inputs provided and should not be relied upon for making significant decisions. Please consult a qualified professional (lawyer, accountant, doctor, etc.) to verify your specific situation. CalculateThis.ai disclaims any liability for damages resulting from the use of this tool.