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Energy consumption

As explained in The AI Pocket Book

The amount of electrical energy used by AI systems during training or inference.

Electricity consumption is the electrical energy AI systems use during training and inference. Measuring it is harder than it sounds: providers report inconsistently, studies use mixed units, and some estimates rely on indirect assumptions. section 6.9

Table 6.1 Average electricity consumption across different models compared with typical household consumption

kWh / 1,000 responses

% of daily household kWh (US)

% of daily household kWh (UK)

Text generation

0.047

0.15%

0.5%

Image generation

2.907

9.83%

30%

Compare text and image generation by 1,000 uses: image generation consumes far more electricity than text generation, and intensive use can accumulate quickly. section 6.9

Why usage volume matters

A single AI request may seem minor, but a coder can generate hundreds of autocompletions per hour. Search engines and online stores may also trigger AI queries, adding consumption without users directly opening an AI tool. section 6.9

The sensible takeaway is not to treat one reported figure as universal. Consumption varies by model and task, while comparisons may express the impact as electricity, carbon emissions, or equivalent household use. section 6.9

How The AI Pocket Book covers it

How deep it goes
Defines the issue, quantifies model-use examples, and flags inconsistent measurement without deriving energy estimates.
Examples
Uses text generation, image generation, coding autocompletions, search queries, household consumption, and petrol-car emissions.
What you can do after
Evaluate AI energy claims by checking usage scale, units, model differences, and whether estimates are direct or indirect.
How it connects
Ties AI model training and inference to GPUs, household electricity, resource consumption, and CO2 emissions.

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