TeleRAG introduces a novel approach to Retrieval-Augmented Generation that significantly reduces inference latency while minimizing GPU memory requirements.

• Implements lookahead retrieval that predicts future LLM outputs to prefetch relevant documents
• Optimizes GPU-CPU data transfer for efficient handling of large knowledge bases
• Achieves substantial latency improvements with minimal accuracy trade-offs
• Enables more responsive AI systems even with limited GPU resources

This research addresses critical engineering challenges in deploying RAG systems at scale, allowing organizations to build more responsive AI applications without requiring extensive hardware upgrades.

TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval