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There is no universal price for running an AI model. A defensible estimate starts by defining the workload and then multiplying measured or documented device power by runtime and utilization, applying the data center’s power-usage effectiveness (PUE), and multiplying the resulting facility energy by the local electricity tariff. A complete data-center figure adds hardware, networking, storage, software, labor, maintenance and capital costs.
Use three separate outputs: IT energy in kilowatt-hours, whole-facility electricity cost in currency, and total cost of ownership. Keeping those measures separate prevents a cloud GPU-hour price, a nameplate wattage and a utility bill from being mixed into one misleading number.
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What exactly are you trying to cost?
State the accounting boundary before collecting numbers. The answer changes substantially depending on whether you are measuring one training run, a fine-tuning job, batch inference, always-on online inference, a single day, or the model’s entire operating life.
- Workload: training, fine-tuning, batch inference or online inference.
- Time window: one run, one day, one month, a release cycle or the full model life cycle.
- Physical boundary: accelerator and server power only, all IT equipment, or the whole facility including cooling and power distribution.
- Financial boundary: electricity alone, a cloud invoice, operating expense, or total cost of ownership (TCO).
- Geography and contract: the actual data-center site or cloud region, tariff, demand charges and reservation terms.
Write this definition at the top of the worksheet. For example: “Energy for one 12-hour training run, measured at the IT plug, plus facility overhead, using the site tariff.” That sentence makes later comparisons auditable.
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How do you calculate AI-model electricity use?
Use a bottom-up calculation that starts with the devices doing the work. The transparent first-pass equations are:
IT energy (kWh) = number of devices × average device power (kW) × elapsed hours × utilization adjustment
Facility energy (kWh) = IT energy × PUE
Electricity cost = facility energy × tariff ($/kWh)For servers with several accelerators, either count each accelerator or use a measured server power figure, but do not count both. If your power reading already represents the average draw during the workload, set the utilization adjustment to 1.0; applying a second utilization factor would understate energy. Apply an adjustment only when you are starting with a rated or maximum power value and need to represent duty cycle, idle periods or throttling.
Keep units consistent. Convert watts to kilowatts by dividing by 1,000, and convert minutes to fractional hours. Record whether the reading is direct-current power at a device, alternating-current power at a rack, or utility-meter energy. PUE belongs after an IT-side measurement; multiplying a utility-meter reading by PUE would count facility overhead twice.
Which hardware and workload measurements do you need?
A useful estimate needs more than a GPU model number. Gather the following for the exact run or service period:
- Accelerator count and type: include GPUs, tensor processors or other accelerators, and note whether a server contains additional accelerators that are idle.
- Average power: prefer telemetry sampled during the job. If unavailable, document the rated power, maximum power and the assumption used to turn it into an average.
- Runtime: capture start and stop timestamps, including restart time and data-loading intervals if machines remain powered.
- Duty cycle and utilization: measure accelerator utilization and account for CPU, memory, storage and network activity. A low accelerator-utilization percentage does not mean the server draws proportionally little power.
- Configuration: record batch size, sequence length, precision, power caps, checkpointing and data-parallel or model-parallel layout.
- Non-accelerator equipment: include host CPUs, memory, local disks, switches and storage servers when your boundary is all IT rather than accelerators only.
LBNL’s bottom-up method characterizes servers, storage and networking, applies rated-power, utilization and idle-power assumptions, sums annual IT electricity and then applies modeled PUE. That approach is useful when direct telemetry is unavailable, but label every assumption so a measured value is not mistaken for a modeled one.
How should utilization and power ratings be handled?
Nameplate power is a ceiling or design reference, not a guaranteed draw. Modern accelerators can operate below their maximum because of memory stalls, communication waits, thermal limits or an explicit power cap. Conversely, CPUs, memory and fans can keep server power high while accelerator utilization is low.
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Average the readings over the complete workload interval, including warm-up, evaluation, checkpointing and pauses during which the node remains on. Use that average directly in the IT-energy equation. Keep the sampling interval and meter location in your notes.
When you only have rated power
Use rated or maximum power as a starting point and apply an explicit utilization or duty-cycle assumption. Run at least three cases, such as low, central and high utilization, rather than presenting one precise-looking result. Explain whether the adjustment represents accelerator activity, whole-server duty cycle or both.
When workloads share a node
Attribute power by measured job time or an agreed allocation rule. If two jobs overlap, do not assign the entire node draw to each job. A proportional allocation based on measured accelerator time is more defensible than dividing by nominal request size.
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What is PUE and how much does cooling add?
Power-usage effectiveness is the ratio of total facility electricity to IT electricity measured at the plug. The amount above 1.0 covers cooling, fans, pumps, power distribution and related infrastructure. A PUE of 1.145 means that 1 kWh delivered to IT corresponds to 1.145 kWh at the facility boundary under that definition.
Use a measured site PUE when available and state its period and boundary. If it is unavailable, disclose an assumed value and show sensitivity. LBNL estimates average PUE for facilities serving AI equipment at 1.145 in 2024 and 1.136 in 2030 in its modeled outlook; those are fleet-level estimates, not a guarantee for a particular building.
| PUE sensitivity case | Facility energy relative to IT energy | Facility overhead relative to IT energy |
|---|---|---|
| 1.10 | 1.10 times IT energy | 10% |
| 1.15 | 1.15 times IT energy | 15% |
| 1.30 | 1.30 times IT energy | 30% |
Climate, redundancy, air or liquid cooling, part-load operation and electrical distribution design all influence PUE. A lower PUE does not automatically produce a lower cost per useful token: a site can save overhead while using less efficient hardware or operating at poor utilization.
Which electricity price should you use?
Use the tariff that applies to the actual site or cloud contract. Include energy charges, demand charges, time-of-use periods, taxes, delivery fees and renewable-power arrangements when they affect the effective price paid for the workload.
The U.S. Energy Information Administration reports a 2024 U.S. average industrial electricity price of 8.13 cents per kWh. Treat that figure as a benchmark, not as a universal AI-data-center rate. A large facility may have a wholesale contract, a negotiated industrial tariff or substantial demand charges that produce a different effective price.
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What does a worked electricity calculation look like?
Consider an illustrative run with 256 accelerators, each assigned 0.7 kW, running for 12 hours at a 70% effective utilization adjustment. The calculation is:
- Compute IT energy: 256 × 0.7 kW × 12 hours × 0.70 = 1,505.3 kWh.
- Apply a PUE of 1.145: 1,505.3 × 1.145 = approximately 1,723.6 kWh at the facility boundary.
- Apply an electricity price of $0.0813 per kWh: 1,723.6 × $0.0813 = approximately $140.15.
This is an illustrative calculation, not a measured claim. Replace the accelerator power, utilization, PUE and tariff with values from your own telemetry and contract. If 0.7 kW is already the measured average draw under load rather than a rated value, omit the 0.70 adjustment and recalculate.
For reporting, publish a range. Vary runtime, average power, utilization, PUE and tariff independently or in coherent low, central and high cases. Rounding the final result to sensible precision communicates uncertainty better than reporting many decimal places.
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How do you estimate the cost of a cloud-hosted model?
A cloud bill is not obtained by multiplying a guessed wattage by a GPU-hour price. Providers normally charge for the commercial resources in the contract, while physical energy is an engineering estimate based on telemetry or a documented model.
Build the invoice estimate from the provider’s actual rate card and usage records:
- GPU-hours or accelerator-hours, including the instance type and count.
- Instance-hour charges for host CPUs, memory and attached operating-system resources.
- Storage capacity, storage operations and backup or snapshot retention.
- Network egress, inter-zone traffic and data-transfer fees.
- Software licenses, managed-service surcharges and support tiers.
- Reservation, commitment, spot or minimum-spend terms that change the effective rate.
For a cloud comparison, report two columns: billed dollars and estimated physical kWh. The billed figure answers the finance question; the kWh figure supports energy, emissions or capacity planning. Do not infer a provider’s facility PUE from a public GPU-hour price, and do not treat a generic plug-in watt meter as a measurement of a hosted workload.
What belongs in total data-center cost?
Electricity is one operating-cost component. A full cost model adds the resources required to own or reserve capacity and to keep it available.
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| Cost category | Examples to include | Typical allocation question |
|---|---|---|
| Compute hardware | Accelerator and server purchase, rental or depreciation | How many useful model-hours does the hardware deliver before replacement? |
| Facility and power | Electricity, demand charges, backup generation and power distribution | Are fixed charges allocated by peak demand, reserved capacity or actual use? |
| Cooling | Chillers, pumps, fans, liquid-cooling loops and maintenance | Is cooling captured in measured PUE or budgeted separately? |
| Networking and storage | Fabric switches, interconnects, storage media and data movement | Does the job’s traffic or dataset require dedicated capacity? |
| Software | Schedulers, observability, operating systems and commercial licenses | Is the fee per node, user, token or subscription period? |
| People and operations | Engineering, site operations, security and support labor | What share of staff time keeps this service reliable? |
| Capital and property | Land, building, financing, taxes and fit-out | How is reserved capacity charged when utilization is below design? |
Epoch AI’s stylized 1-GW model makes energy cost a function of electricity price, IT capacity, PUE and utilization, then adds maintenance, labor and other ownership costs. The lesson is to keep energy, electricity cost and TCO as separate outputs even when they share assumptions.
How should you compare two models or deployments?
Compare useful work, not just device count or a single power figure. A deployment with a lower PUE can still cost more if its hardware produces fewer useful tokens per watt or spends more time idle.
| Comparison axis | What to measure | Why it matters |
|---|---|---|
| Training energy | kWh per complete training run or per training token | Captures both efficiency and the amount of work completed. |
| Inference energy | kWh per million output or input-output tokens | Normalizes different request volumes and sequence lengths. |
| Financial cost | Dollars per run, per million tokens or per service day | Reflects tariffs or cloud prices rather than physical energy alone. |
| Useful throughput | Tokens per second or completed jobs per hour | Shows whether a lower-power system actually delivers work faster. |
| Latency | Median and tail response time under the target load | Prevents an energy-efficient but unusably slow service from appearing superior. |
| Utilization | Accelerator and whole-node utilization over the billing period | Exposes idle capacity and makes reserved hardware comparable. |
| PUE | Measured or assumed facility multiplier | Separates IT efficiency from building overhead. |
| Carbon intensity | Grid or contract emissions factor paired with kWh | Energy and climate impact are related but not identical. |
| TCO | Energy, hardware, facility, labor, maintenance, networking and storage | Shows the cost of sustaining the service over its useful life. |
Hold the workload constant when comparing systems: same model version, precision, batch or request mix, sequence-length distribution, quality target and reporting interval. Otherwise a result may reflect different work rather than a better system.
How do you represent uncertainty honestly?
Every input has a confidence level. Mark values as measured, vendor-rated, contractually billed, modeled or assumed. Preserve the meter boundary and timestamp so another analyst can reproduce the calculation.
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Create low, central and high cases by varying the inputs that dominate the result. Runtime and accelerator count are often known closely, while average power, utilization, PUE and effective tariff may be uncertain. Show the resulting kWh and dollars for each case rather than hiding uncertainty in one rounded number.
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Test sensitivity
Change one input at a time to identify leverage. If a small PUE change barely moves the result but utilization changes it dramatically, investment in scheduling or batching may matter more than a cooling retrofit. If the tariff dominates, time-of-use scheduling or a different region may be the important decision.
Document exclusions
State whether data preparation, failed runs, idle standby, network transfer, storage replication, backup power losses and staff time are included. An exclusion is acceptable when it is explicit; silently omitting it makes two estimates impossible to compare.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does the industry-scale context show?
At national scale, the same accounting choices matter. The Congressional Research Service reports that U.S. data centers used approximately 176 TWh in 2023, about 4.4% of U.S. electricity use. This is total data-center consumption, not the energy of one AI model.
LBNL’s reference case estimates 649 TWh of data-center electricity in 2030. Its compounded-uncertainty scenarios span 521 to 843 TWh in 2030. These are modeled outlooks, so they should be used as context for planning rather than as a plug-in value for an individual workload.
Large totals do not remove the need for bottom-up measurement. A small model run can have a high per-token cost if it runs on underutilized reserved hardware, while a larger model can achieve a lower unit cost through batching and high sustained utilization.
Which mistakes make estimates unreliable?
- Calling a GPU-hour price an electricity price: the invoice includes capacity, service and margin, not just energy.
- Multiplying measured average power by utilization again: this double-adjusts the same workload.
- Applying PUE to a utility-meter reading: facility energy already includes overhead.
- Using nameplate power as actual draw: rated power ignores idle, throttling and host-system behavior.
- Ignoring non-accelerator loads: CPUs, memory, storage, network switches and cooling can be material.
- Reporting a national average tariff as a site rate: demand charges and contracts can change the effective price.
- Mixing boundaries between options: one estimate may include storage and labor while another includes only GPUs.
- Hiding uncertainty behind precise decimals: uncertain inputs do not justify false precision.
- Comparing different work: token counts, sequence lengths, quality targets and batch sizes must match.
What is a repeatable workflow for a new model?
Use this sequence for every training or inference estimate:
- Write the boundary: name the workload, period, meter boundary, geography and financial scope.
- Inventory equipment: list accelerators, servers, storage and networking, including quantities and power ratings.
- Collect telemetry: export device, node, rack or utility readings for the complete workload interval.
- Normalize units: convert watts to kilowatts and minutes to hours; record sampling intervals.
- Calculate IT energy: use measured average power, or apply a documented utilization adjustment to rated power.
- Select PUE: use measured site data or disclose an assumed value with sensitivity cases.
- Apply the tariff: include energy, demand and time-of-use components relevant to the site or contract.
- Build the cloud or TCO view: add GPU-hour, instance, storage, network, labor, maintenance and capital charges as applicable.
- Publish ranges: show low, central and high results, identify the dominant assumptions and state exclusions.
- Recheck after deployment: compare the estimate with metered energy and update power, utilization and PUE assumptions.
This workflow produces an estimate that can be audited, updated and compared across model versions without pretending that one number applies to every deployment.
Frequently asked questions
Can I convert a model’s token count directly into electricity?
Token count alone is insufficient because energy also depends on sequence length, batching, hardware, precision, utilization and cooling overhead. Measure or estimate kWh for a representative request mix, then divide by the corresponding token count to derive a workload-specific intensity.
Should idle servers be charged to a model?
If the servers are reserved or powered for that model, include their standby energy in the service-period boundary, allocating shared idle capacity by a stated rule. For a short batch job on shared infrastructure, report both incremental energy and the broader reserved-capacity cost so readers can see the difference.
How often should an estimate be refreshed?
Refresh it when hardware, software precision, batching, power caps, facility PUE, cloud pricing or the electricity contract changes. For a continuously operated service, a monthly reconciliation against metered energy and invoices will reveal drift that a one-time benchmark misses.
How can I make the result useful for emissions reporting?
Keep the physical kWh result separate, then pair it with the grid or contractual emissions factor for the same location and time period. Document whether the factor is location-based or market-based; changing that accounting choice changes emissions without changing the model’s electricity use.
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What evidence should accompany a published estimate?
Include the workload definition, hardware inventory, runtime, power source and meter boundary, PUE value, tariff date, utilization method, exclusions and scenario range. Retain raw telemetry or billing records so another analyst can reproduce the arithmetic.
Frequently Asked Questions
Can I convert a model’s token count directly into electricity?
Token count alone is insufficient because energy also depends on sequence length, batching, hardware, precision, utilization and cooling overhead. Measure or estimate kWh for a representative request mix, then divide by the corresponding token count to derive a workload-specific intensity.
Should idle servers be charged to a model?
If the servers are reserved or powered for that model, include their standby energy in the service-period boundary, allocating shared idle capacity by a stated rule. For a short batch job on shared infrastructure, report both incremental energy and the broader reserved-capacity cost so readers can see the difference.
How often should an estimate be refreshed?
Refresh it when hardware, software precision, batching, power caps, facility PUE, cloud pricing or the electricity contract changes. For a continuously operated service, a monthly reconciliation against metered energy and invoices will reveal drift that a one-time benchmark misses.
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How can I make the result useful for emissions reporting?
Keep the physical kWh result separate, then pair it with the grid or contractual emissions factor for the same location and time period. Document whether the factor is location-based or market-based; changing that accounting choice changes emissions without changing the model’s electricity use.
What evidence should accompany a published estimate?
Include the workload definition, hardware inventory, runtime, power source and meter boundary, PUE value, tariff date, utilization method, exclusions and scenario range. Retain raw telemetry or billing records so another analyst can reproduce the arithmetic.
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