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This article provides empirical data on the energy consumption and operational costs of running five specific large language models locally on Apple Silicon hardware, directly addressing infrastructure efficiency for AI deployment. By measuring real-world wattage against a fixed electricity rate, it offers critical insights into the sustainability and economic viability of edge-based generative AI solutions compared to traditional GPU setups.
Sources coveringTowards Data Science, Quantum Computing Report, SiliconANGLE, chronicle.lu
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

How Much Does a Local LLM Actually Cost to Run? I Measured Every Watt on Apple Silicon

Towards Data Science1 min read

Five models, sustained generation, real wall-socket energy at $0.31/kWh — and the surprise the RTX-3090 numbers predicted, only bigger. The post How Much Does a Local LLM Actually Cost to Run? I Measured Every Watt on Apple Silicon appeared first on Towards Data Science.

Read the full article at Towards Data Science

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