TensorOpera Router: A Multi-Model Router for Efficient LLM Inference
Dimitris Stripelis, Zhaozhuo Xu, Zijian Hu, Alay Dilipbhai Shah, Han Jin, Yuhang Yao, Jipeng Zhang, Tong Zhang
Abstract
With the rapid growth of Large Language Models (LLMs) across various domains, numerous new LLMs have emerged, each possessing domain-specific expertise. This proliferation has highlighted the need for quick, high-quality, and cost-effective LLM query response methods. Yet, no single LLM exists to efficiently balance this trilemma. Some models are powerful but extremely costly, while others are fast and inexpensive but qualitatively inferior. To address this challenge, we present TO-Router, a non-monolithic LLM querying system that seamlessly integrates various LLM experts into a single query interface and dynamically routes incoming queries to the most high-performant expert based on query’s requirements. Through extensive experiments, we demonstrate that when compared to standalone expert models, TO-Router improves query efficiency by up to 40%, and leads to significant cost reductions of up to 30%, while maintaining or enhancing model performance by up to 10%.
BibTeX
@inproceedings{stripelis-etal-2024-tensoropera,
title = "{T}ensor{O}pera Router: A Multi-Model Router for Efficient {LLM} Inference",
author = "Stripelis, Dimitris and
Xu, Zhaozhuo and
Hu, Zijian and
Shah, Alay Dilipbhai and
Jin, Han and
Yao, Yuhang and
Zhang, Jipeng and
Zhang, Tong and
Avestimehr, Salman and
He, Chaoyang",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.34/",
doi = "10.18653/v1/2024.emnlp-industry.34",
pages = "452--462"
}