EACO-RAG introduces a novel distributed approach that combines small language models on edge devices with cloud-based large language models for optimal performance.

• Creates a tiered deployment architecture that balances efficiency and capability
• Implements adaptive knowledge update mechanisms to maintain current information
• Enables collaborative retrieval between edge devices to reduce cloud dependencies
• Preserves data locality for improved privacy and reduced latency

This research provides a practical solution for organizations seeking to deploy powerful language models while managing computational constraints and privacy concerns.

EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update