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arXiv AI By Carolina Fortuna, Vid Han\v{z}el, Tim Strnad, Bla\v{z} Bertalani\v{c}

Where Should Agents Live? Energy-Memory Characterization of Agentic AI for the Edge-Cloud Continuum

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The paper introduces agentic-eCAL, an extension of the Energy Cost of AI Lifecycle metric to evaluate multi‑agent AI workflows across the edge‑cloud continuum. By combining a two‑rate energy model with OSI‑layer transport analysis, the authors quantify that inter‑agent text transfer accounts for only 0.25% of total workflow energy, highlighting that the main energy cost lies in additional inference and context processing triggered by communication. The study uses extensive GPU benchmarks on NVIDIA A100/H100 with 16 open‑weight models and 8 orchestration topologies to validate the metric and explore placement implications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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