Energy consumption
Energy consumption is the amount of energy used by a system or process over a period of operation; its rate of use is power consumption.
Introduction to Generative AI, Second Edition
Energy consumption is the amount of electrical or other energy consumed by a computational process, system, or activity.
- How it is used
- Frames energy consumption as an environmental and accountability cost of training and deploying large language models.
- How deep it goes
- Explains training electricity, inference, cooling, estimates, and measurement tools without providing a full audit procedure.
- Examples
- Uses a GPT-3 emissions estimate and CodeCarbon’s hardware measurements as concrete reference points.
- What you can do after
- Evaluate/understand it by questioning emissions estimates, looking for energy measurements, and weighing resource demands against public benefit.
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The Art of Code
Energy consumption is the amount of electrical or other energy required to operate software and its supporting computing systems.
- How it is used
- Frames sustainable coding as reducing infrastructure and electricity impact while delivering acceptable user responsiveness.
- How deep it goes
- Explains lifecycle context, efficiency tradeoffs, measurement approaches, and lean storage without detailing energy-estimation implementations.
- Examples
- Uses server lifespans, obsolete devices, a PostgreSQL customer-table exercise, microbenchmarks, and CPU-memory profilers.
- What you can do after
- Apply it via microbenchmarks and profilers to compare runtime resource behavior and reduce avoidable storage waste.
Digital Twins in Action
Electricity consumption is the amount of electrical energy consumed by a system or facility.
- How it is used
- Guides home digital-twin decisions about appliance timing, heating and cooling, solar self-consumption, and electricity costs.
- How deep it goes
- Develops baselines, measurement choices, sensor update rates, and an AutoML forecasting workflow, while noting that appliance-driven demand can spike.
- Examples
- Uses a monthly bill target, meter-box and appliance sensors, and daily plots of electricity use, grid flows, and weather.
- What you can do after
- Apply it via AutoGluon TimeSeriesPredictor to forecast grid imports and inform electricity-use decisions.
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Build a Text-to-Image Generator (from Scratch)
The amount of electrical energy consumed by computing processes such as model training and inference.
- How it is used
- Frames energy consumption as an environmental cost and scalability constraint in text-to-image model development and deployment.
- How deep it goes
- Explains resource drivers and mitigation techniques but stops short of implementation details or quantitative energy measurement.
- Examples
- Uses large datasets, powerful hardware, efficiency techniques, and a projected 2040 global-energy scenario as concrete reference points.
- What you can do after
- Evaluate computational-footprint trade-offs involving model quality, scalability, and environmental impact.
Read how Build a Text-to-Image Generator (from Scratch) explains energy consumption →
The AI Pocket Book
The amount of electrical energy used by AI systems during training or inference.
- How it is used
- Frames electricity consumption as a practical environmental and resource-cost consideration for AI training and use.
- 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.
Related concepts
- CodeCarbonmeasured by · CodeCarbon is an open-source Python library that estimates the energy consumption and carbon-dioxide-equivalent emissions of computational workloads.
- language modelmeasured by · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- heat mapused by · A heat map is a two-dimensional visualization that encodes numeric values, frequencies, or activity using color intensity or hue.
- quantizationaddressed by · Quantization maps numerical values to a finite set of discrete representations, often using reduced precision to lower model storage or computation requirements.
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