[2511.07885] Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2511.07885 (cs)

[Submitted on 11 Nov 2025 (v1), last revised 7 Aug 2026 (this version, v5)]

Title:Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

Authors:Jon Saad-Falcon, Avanika Narayan, Hakki Orhun Akengin, J. Wes Griffin, Herumb Shandilya, Adrian Gamarra Lafuente, Medhya Goel, Rebecca Joseph, Shlok Natarajan, Etash Kumar Guha, Shang Zhu, Ben Athiwaratkun, John Hennessy, Azalia Mirhoseini, Christopher Ré<br>View a PDF of the paper titled Intelligence per Watt: Measuring Intelligence Efficiency of Local AI, by Jon Saad-Falcon and 14 other authors

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Abstract:Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure. Demand growth strains this paradigm faster than providers can scale. Two advances create an opportunity to rethink it: small, local LMs (

Subjects:

Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as:<br>arXiv:2511.07885 [cs.DC]

(or<br>arXiv:2511.07885v5 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2511.07885

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arXiv-issued DOI via DataCite

Submission history<br>From: Jon Saad-Falcon [view email]<br>[v1]<br>Tue, 11 Nov 2025 06:33:30 UTC (5,373 KB)

[v2]<br>Fri, 14 Nov 2025 00:53:12 UTC (5,538 KB)

[v3]<br>Thu, 26 Feb 2026 17:09:14 UTC (5,538 KB)

[v4]<br>Thu, 21 May 2026 03:40:21 UTC (5,134 KB)

[v5]<br>Fri, 7 Aug 2026 02:40:27 UTC (5,621 KB)

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