<![CDATA[Intelligence per Watt: Measuring Intelligence Efficiency of Local AI]]>The authors used Intelligence per Watt as a metric to evaluate the efficiency of local AI inference, and found that local models under 20 billion active parameters can successfully answer 88.7 percent of single turn chat and reasoning queries. Between 2023 and 2025, the intelligence efficiency of these models improved by a factor of 5.3 due to advances in both model architectures and hardware accelerators.

Local models are now capable of handling the vast majority of everyday user requests without relying on any centralized cloud infrastructure. While cloud models are still better at highly specialized reasoning, deploying small models on personal devices is quickly becoming a practical and energy efficient alternative.

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Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

Abstract page for arXiv paper 2511.07885: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

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arXiv.org (arxiv.org)
]]>https://citiverse.it/topic/3d865593-ab9f-4583-b97f-198f473d5327/intelligence-per-watt-measuring-intelligence-efficiency-of-local-aiRSS for NodeSat, 26 Sep 2026 04:16:57 GMTWed, 16 Sep 2026 14:05:59 GMT60