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cs.CY2025★ 2 cited
Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency
Erik Johannes Husom, Arda Goknil, Merve Astekin +5
Deploying Large Language Models (LLMs) on edge devices presents significant challenges due to computational constraints, memory limitations, inference speed, and energy consumption…
cs.CY2024★ 4 cited
The Price of Prompting: Profiling Energy Use in Large Language Models Inference
Erik Johannes Husom, Arda Goknil, Lwin Khin Shar +1
In the rapidly evolving realm of artificial intelligence, deploying large language models (LLMs) poses increasingly pressing computational and environmental challenges. This paper…