Toward Sustainable On-Device Intelligence: A Survey on Energy-Efficient RAG Systems with Small Language Models
Abstract
Abstract The proliferation of Large Language Models (LLMs) has driven a paradigm shift toward on-device inference, motivated by privacy preservation, latency reduction, and offline capability. Simultaneously, Retrieval-Augmented Generation (RAG) has emerged as the dominant pattern for grounding language model outputs in external knowledge. However, deploying RAG pipelines on resource-constrained edge devices—smartphones, IoT gateways, and embedded systems equipped with Neural Processing Units (NPUs)—introduces a complex multi-dimensional trade-off among accuracy, latency, memory footprint, energy consumption, and carbon emissions. While each pillar has been surveyed individually, the three-way intersection of on-device AI inference optimization, retrieval-augmented generation, and Green AI / sustainability has not previously been drawn together into a unified system-design perspective, and this survey undertakes that cross-domain synthesis. We systematically examine (i) model compression techniques—quantization, pruning, knowledge distillation—with emphasis on NPU-aware optimization; (ii) on-device RAG system architectures including lightweight retrieval, context compression, and modular frameworks; and (iii) energy measurement, carbon footprint estimation, and sustainable inference strategies for edge deployment. As our core contribution, we propose ALEMC, an evaluation framework extending conventional metrics with explicit Energy and Carbon dimensions, and synthesize…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.