LLMs consume 5.4x less mobile energy than ad-supported web search
The standard AI energy debate compares server-side LLM inference to a server-side Google query. I think this misses most of what actually happens on a mobile device during a real search session. I built a parametric model of the full end-to-end mobile search session: 4G/5G radio energy, SoC rendering cost for a 2.5MB page, programmatic advertising RTB auctions running in the background, and network transmission costs for both sides. Then compared it to an equivalent LLM session. Main finding across 10,000 Monte Carlo draws: on mobile, a standard LLM session uses on average 5.4x less…
In plain words
This is a parametric energy consumption model comparing mobile LLM sessions to ad-supported web search. It accounts for end-to-end mobile factors including 4G/5G radio energy, device rendering costs for typical web pages, programmatic advertising overhead, and network transmission. The analysis finds that LLM sessions consume approximately 5.4x less mobile battery energy than equivalent web searches, with programmatic ads contributing up to 41% of per-session device drain. The model is based on Monte Carlo simulations rather than empirical device testing and shows different results for Wi-Fi connections and reasoning models.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
The standard AI energy debate compares server-side LLM inference to a server-side Google query. I think this misses most of what actually happens on a mobile device during a real search session. I built a parametric model of the full end-to-end mobile search session: 4G/5G radio energy, SoC rendering cost for a 2.5MB page, programmatic advertising RTB auctions running in the background, and network transmission costs for both sides. Then compared it to an equivalent LLM session. Main finding across 10,000 Monte Carlo draws: on mobile, a standard LLM session uses on average 5.4x less energy than a classic ad-supported web search session. Programmatic advertising alone accounts for up to 41% of device battery drain per session. Caveats I tried to be explicit about: - Advantage disappears on fixed Wi-Fi/fiber - Reverses for reasoning models - Parametric model, not empirical device measurement. Greenspector has offered to run terminal measurements for v2 - Jevons paradox applies SSRN working paper, not peer-reviewed. Methodology and Monte Carlo distributions fully documented in the paper. Happy to defend the assumptions. DOI: 10.2139/ssrn.6287918
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