Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency

• URL: http://arxiv.org/abs/2504.03360v1
• Date: Fri, 04 Apr 2025 11:29:30 GMT
• Title: Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency
• Authors: Erik Johannes Husom, Arda Goknil, Merve Astekin, Lwin Khin Shar, Andre Kåsen, Sagar Sen, Benedikt Andreas Mithassel, Ahmet Soylu,
• Abstract summary: We conduct a comprehensive analysis of 28 quantized Large Language Models (LLMs) from the Ollama library.<n>We evaluate energy efficiency, inference performance, and output accuracy across multiple quantization levels and task types.<n>Our findings reveal the trade-offs between energy efficiency, inference speed, and accuracy in different quantization settings.
• Score: 6.306413686006502
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Deploying Large Language Models (LLMs) on edge devices presents significant challenges due to computational constraints, memory limitations, inference speed, and energy consumption. Model quantization has emerged as a key technique to enable efficient LLM inference by reducing model size and computational overhead. In this study, we conduct a comprehensive analysis of 28 quantized LLMs from the Ollama library, which applies by default Post-Training Quantization (PTQ) and weight-only quantization techniques, deployed on an edge device (Raspberry Pi 4 with 4GB RAM). We evaluate energy efficiency, inference performance, and output accuracy across multiple quantization levels and task types. Models are benchmarked on five standardized datasets (CommonsenseQA, BIG-Bench Hard, TruthfulQA, GSM8K, and HumanEval), and we employ a high-resolution, hardware-based energy measurement tool to capture real-world power consumption. Our findings reveal the trade-offs between energy efficiency, inference speed, and accuracy in different quantization settings, highlighting configurations that optimize LLM deployment for resource-constrained environments. By integrating hardware-level energy profiling with LLM benchmarking, this study provides actionable insights for sustainable AI, bridging a critical gap in existing research on energy-aware LLM deployment.

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