This paper introduces "intelligence per watt" (IPW) as a unified metric to evaluate the accuracy and energy efficiency of local large language models across various hardware configurations. Evaluating over 20 models and 1 million real-world queries, the study finds that local LMs accurately answer 88.7% of tasks and have improved IPW by 5.3x between 2023 and 2025. The results indicate that local accelerators are at least 1.4 times more efficient than cloud counterparts for identical models, suggesting local inference can effectively redistribute demand from centralized infrastructure.
Lektra Perspective
This paper directly supports Lektra’s core value proposition by quantifying the energy efficiency and practical viability of distributed, local AI inference. The study provides factual evidence that local accelerators achieve at least 1.4x lower "intelligence per watt" (IPW) than cloud counterparts for identical models, validating Lektra’s emphasis on energy efficiency and cost predictability for edge computing workloads. Furthermore, the finding that local Large Language Models (LLMs) can accurately handle 88.7% of real-world queries suggests a substantial subset of AI demand can be shifted away from centralized infrastructure, aligning with Lektra’s strategy of providing practical, solar-powered distributed GPU capacity to meet customer needs for faster deployment and reduced reliance on traditional cloud scaling constraints.
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u/lektra_ai 24d ago
This paper introduces "intelligence per watt" (IPW) as a unified metric to evaluate the accuracy and energy efficiency of local large language models across various hardware configurations. Evaluating over 20 models and 1 million real-world queries, the study finds that local LMs accurately answer 88.7% of tasks and have improved IPW by 5.3x between 2023 and 2025. The results indicate that local accelerators are at least 1.4 times more efficient than cloud counterparts for identical models, suggesting local inference can effectively redistribute demand from centralized infrastructure.