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What is LLMs consume 5.4x less mobile energy than ad?

This tool analyses the energy consumption of large language models compared to ad-supported web services. Rather than measuring energy per individual server query, it evaluates full user sessions, showing that LLMs consume approximately 5.4 times less mobile energy than browsing ad-supported websites. The analysis is based on thermodynamic efficiency metrics that account for the hidden energy costs of serving advertisements, tracking pixels, and associated infrastructure. The research is relevant to anyone interested in the environmental impact of AI, web sustainability, or comparing the actual resource consumption of different digital services.

Key features

Energy consumption comparison

measures LLM usage against ad-supported web browsing on mobile devices

Session-based analysis

evaluates full user sessions rather than isolated queries for more realistic comparisons

Thermodynamic efficiency framework

applies physics-based measurement to digital service consumption

Supported web search functionality

integrates search capabilities within the assessment tool

Pros & cons

Advantages

  • Provides a different perspective on AI energy consumption by accounting for the full cost of ad-supported alternatives
  • Uses session-level measurement which better reflects real-world usage patterns
  • Freely available for basic access, making the research accessible to a wide audience
  • Helps inform discussions about the environmental trade-offs between different digital services

Limitations

  • The assessment methodology may not account for all variables affecting energy consumption across different network conditions and device types
  • Limited scope focusing primarily on mobile energy usage rather than broader infrastructure impacts

Use cases

Researchers studying the environmental impact of artificial intelligence and digital services

Content strategy teams evaluating the sustainability implications of different web architectures

Policy makers assessing the environmental costs of ad-supported versus AI-based information access

Technology companies comparing the resource efficiency of their services

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