Artificial intelligence is transforming industries and reshaping economies, but its energy consumption is raising serious concerns. AI models require vast amounts of computational power to operate, often relying on energy-intensive data centers that consume significant electricity. Not all AI tasks are created equal—some, like image generation, are far more energy-intensive than simpler tasks like text classification. Geographically, these impacts also vary depending on regional energy sources and infrastructure.
HuggingFace's AI Energy Score aims to provide clarity in this complex landscape by systematically testing hundreds of models across various tasks. By offering a standardized framework for measuring energy efficiency, it seeks to empower developers and policymakers to make informed decisions about sustainable AI deployment while considering the broader implications for energy systems worldwide.
The chart above illustrates the average energy consumption across ten common AI tasks. Image generation tasks stand out as particularly energy-intensive due to their reliance on large-scale models like Stable Diffusion, while binary text classification consumes minimal energy. These differences highlight the need for tailored approaches to optimizing AI energy use.
The most energy-efficient models include distilgpt2, opt-125m, and gpt2-medium. These models consume minimal GPU energy while maintaining high performance. Distilgpt2 is particularly noteworthy for its compact architecture, making it ideal for lightweight applications.
These results demonstrate how optimization can reduce energy costs without sacrificing functionality—a crucial step toward sustainable AI development.
The least energy-efficient models include phi-4 and Llama-2-13b-hf. These models consume significantly more GPU energy due to their large size and computational intensity.
While these models excel at complex tasks like image generation or reasoning, their environmental impact is substantial. Developers must weigh these trade-offs when choosing models for specific applications.
The comparison between efficient and inefficient models highlights the diversity in AI technologies. Smaller models are optimized for efficiency and accessibility, while larger models prioritize performance at a higher energy cost. Policymakers in both tech and energy sectors should consider these intricacies when crafting regulations or frameworks for sustainable AI development.
Abiotic Depletion Potential (ADPe): This metric measures the depletion of non-renewable resources like minerals and metals during electricity production. The USA leads in ADPe due to its reliance on fossil fuels, while France ranks lowest thanks to its nuclear-powered grid.
Primary Energy Consumption (PE): PE represents the total amount of energy required to produce one kilowatt-hour (kWh) of electricity. China has the highest PE value due to its coal-heavy power mix, while France benefits from more efficient nuclear energy production.
The chart above highlights regional differences in electricity generation methods and their environmental impacts. China’s coal-heavy power mix results in higher primary energy consumption compared to France’s reliance on nuclear power, which is more efficient and less resource-intensive.