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AI Data Center Power Consumption Calculator

Calculate energy usage, electricity costs, and carbon footprint for AI training and inference clusters. Includes database of H100, A100, MI300, and TPUs.

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Category System & Hardware
Global avg approx 475g. Nuclear/Hydro <50g.
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About

Deploying Artificial Intelligence infrastructure requires precise energy modeling. Unlike traditional web servers, AI training clusters operate at high thermal design power (TDP) densities, often running GPUs at peak capacity for weeks or months. Miscalculating the power budget affects electrical provisioning, cooling requirements (PUE), and operational expenditure (OpEx).

This tool models the total energy footprint of an AI cluster. It accounts for accelerator power (GPUs/TPUs), server overhead (CPUs, RAM, Networking), cooling efficiency, and utilization rates. Accuracy here is critical for determining the feasibility of large language model (LLM) training runs or sizing the backup power systems for inference nodes.

ai data center energy gpu h100 carbon footprint

Formulas

The calculation splits the power load into IT equipment (Compute) and Infrastructure (Cooling/Losses). The core formula for total power consumption Ptotal is:

Ptotal = (Nnode × [(Ngpu × Pgpu) + Pbase] × Ufactor) × PUE

Where:

  • Nnode is the number of server nodes in the cluster.
  • Ngpu is the count of accelerators per node.
  • Pgpu is the Thermal Design Power (TDP) of a single accelerator in Watts.
  • Pbase is the power draw of the host system (Dual CPUs, RAM, NVMe, Fans).
  • Ufactor is the utilization percentage (0.0 to 1.0).
  • PUE (Power Usage Effectiveness) represents facility efficiency (Total Facility Power ÷ IT Equipment Power).

Total Energy E over a duration t is calculated as:

E = Ptotal × t

Reference Data

Accelerator ModelManufacturerTDP (Watts)Memory (VRAM)InterconnectEst. System Power Adder
H100 SXM5NVIDIA70080 GB HBM3NVLink+400W (per 4 GPUs)
H100 PCIeNVIDIA35080 GB HBM2ePCIe Gen5+200W
A100 SXM4NVIDIA40080 GB HBM2eNVLink+300W
A100 PCIeNVIDIA25040 GB HBM2PCIe Gen4+150W
V100 SXM2NVIDIA30032 GB HBM2NVLink+250W
MI300XAMD750192 GB HBM3Infinity Fabric+450W
MI250XAMD560128 GB HBM2eInfinity Fabric+350W
Instinct MI210AMD22564 GB HBM2ePCIe Gen4+150W
Gaudi 2Intel60096 GB HBM2eEthernet+350W
TPU v4 (Pod)Google220 (est)32 GB HBMICIN/A (Custom)
TPU v5pGoogle450 (est)95 GB HBMICIN/A (Custom)
L40SNVIDIA35048 GB GDDR6PCIe Gen4+150W
RTX 6000 AdaNVIDIA30048 GB GDDR6PCIe Gen4+150W
T4 Tensor CoreNVIDIA7016 GB GDDR6PCIe Gen3+50W

Frequently Asked Questions

PUE (Power Usage Effectiveness) measures data center efficiency. A PUE of 1.5 means for every 1.0 watt used by the GPUs, another 0.5 watts is used for cooling and power distribution. AI clusters generate massive heat densities, often requiring liquid cooling or advanced HVAC, which significantly impacts the PUE and total electricity bill.
GPUs do not run at max TDP 100% of the time, even during training. Checkpointing, data loading, and communication overhead create idle periods. A typical sustained utilization for LLM training might be 60-80%, while inference workloads fluctuate wildly based on user traffic.
Yes. The calculator adds a "System Base Power" estimate to every node. High-end AI nodes typically run Dual EPYC or Xeon processors with TBs of RAM, which can consume 500W to 1000W per node independently of the GPUs.
Carbon intensity (gCO2/kWh) represents the grams of Carbon Dioxide emitted per kilowatt-hour of electricity generated. This varies by region. Coal-heavy grids may be 800g+, while hydro or nuclear grids can be under 50g.