Towards Data Science
Oct 5

How to Build a Cheap, Yet Reliable Model Router With Jev

The article explains how to create an inexpensive yet dependable model router using Jev, highlighting that model routing can now be considered a design decision rather than merely a system improvement. It outlines the steps and benefits of implementing this approach, emphasizing its practicality and cost-effectiveness. The piece serves as a guide for developers looking to integrate efficient model routing into their workflows.

By Thuwarakesh Murallie
arXiv AI
Sep 30

Routing Should Pay for Itself: Sparse Supervision for Economical LLM Routing

The paper introduces SaveRouter, a sparse‑supervision framework for large language model routing that selectively gathers informative model feedback and shares capability information across related queries. By using only about 33–41% of available training feedback, SaveRouter achieves competitive or superior routing quality while reducing the break‑even deployment volume by 1.9–9.5× compared to conventional routers. The study also shows that the supervision level that minimizes serving cost may differ from the one that yields the earliest payback.

By Guannan Lai, Gelin Bian, Hao-Xuan Ma, Jun-Peng Jiang, Long Chen, Jian-Dong Liu, Zhi-Hao Tan, Han-Jia Ye
arXiv Computation and Language
1d ago

Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving

arXiv:2606.27457v2 Announce Type: replace-cross Abstract: Serving open-weight language models on premises forces a trade-off between accuracy and computational cost. Hosting models of different sizes...

By Yasmin Moslem, Magdalena Kacmajor, Vasudevan Nedumpozhimana, Ammar Abbas, Solmaz Panahi, David Lynch, Zhuangzhuang Nie, Alexandros Agapitos, Aleksandar Milenovic, Hongmeng Song, Yucheng Shi, Yue Pan, Patricia Buffini, John D. Kelleher