Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite
arXiv:2606.11257v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generation. Running them entirely on-device benefits privacy, latency, and offline use, but the energy cost of CPU inference is a major barrier. We present what is, to our knowledge, the first end-to-end RAG pipeline that runs all neural stages -- embedding, reranking, and LLM generation -- on the Qualcomm Hexagon NPU of the Snapdragon X Elite. Profiling on a Dell XPS 13 laptop, we compare NPU-accelerated
The proliferation of advanced mobile NPUs and the increasing demand for private, low-latency AI inference are driving innovation in on-device RAG solutions.
This breakthrough demonstrates the technical feasibility of running complex AI pipelines locally, reducing reliance on cloud infrastructure and enhancing privacy for sensitive applications.
The ability to run energy-efficient, full RAG pipelines on mobile devices shifts the paradigm for AI application development, enabling a new class of edge-native intelligence.
- · Qualcomm
- · Dell
- · On-device AI application developers
- · Users prioritizing privacy and offline functionality
- · Cloud AI service providers (for certain use cases)
- · Competitors with less efficient edge AI hardware
Widespread adoption of on-device RAG will enable more private and real-time AI assistance on personal devices.
This could lead to a decentralization of AI compute, with less data flowing to large, centralized cloud providers.
Increased on-device processing capabilities may accelerate the development of autonomous personal AI agents that operate independently of constant internet connectivity.
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Read at arXiv cs.CL