Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain coverage. Modern RAG pipelines rely on large datastores, creating a significant system challenge: achieving high throughput and low latency is difficult, especially when GPU memory is limited. To address these challenges, we propose TeleRAG, an efficient inference system that reduces latency and improves throughput with minimal GPU memory requirements. The core innovation of TeleRAG is lookahead retrieval, a prefetching mechanism that predicts required data and transfers them from CPU to GPU in parallel with LLM generation. In addition, TeleRAG adopts a prefetching scheduler and a cache-aware scheduler to support efficient multi-GPU inference with minimal overhead. Evaluations show TeleRAG achieves up to a 1.53x average end-to-end latency reduction (single-query) and 1.83x higher average throughput (batched), as well as good scalability in throughput. This confirms the practical utility of TeleRAG for faster and more memory-efficient deployments of RAG applications.
@article{arxiv.2502.20969,
title = {TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval},
author = {Chien-Yu Lin and Keisuke Kamahori and Yiyu Liu and Xiaoxiang Shi and Madhav Kashyap and Yile Gu and Rulin Shao and Zihao Ye and Kan Zhu and Rohan Kadekodi and Stephanie Wang and Arvind Krishnamurthy and Luis Ceze and Baris Kasikci},
journal= {arXiv preprint arXiv:2502.20969},
year = {2026}
}