arXiv:2412. 04504v2 Announce Type: replace-cross Abstract: As large language models (LLMs) grow in popularity for their diverse capabilities, improving the efficiency of their inference systems has become increasingly critical.
By Ozgur Guldogan, Jackson Kunde, Kangwook Lee, Ramtin Pedarsani
arXiv:2607. 09172v1 Announce Type: cross Abstract: Large Language Models are reshaping how software is developed and maintained.
By Nada Zine, Tristan Coignion, Vincenzo Stoico, Cl\'ement Quinton, Romain Rouvoy, Patricia Lago
arXiv:2505. 07833v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) improves the reliability of large language models by integrating external knowledge, but serving RAG pipelines efficiently is challenging because requests traverse heterogeneous components spanning LLM inference, databases, and CPU-side processing.
By Saurabh Agarwal, Bodun Hu, Luis Pabon, Myungjin Lee, Jayanth Srinivasa, Aditya Akella
arXiv:2508. 06133v4 Announce Type: replace-cross Abstract: We study offline scheduling for large language model (LLM) serving under a fixed KV-cache memory budget, where requests have heterogeneous prompt (prefill) and response (decode) lengths.
By Meixuan Wang, Yinyu Ye, Zijie Zhou
arXiv:2607. 18253v1 Announce Type: new Abstract: Modern language query routers improve inference efficiency by assigning each query to a model that balances response quality and monetary cost.
By Shivam Patel, Akaash R. Parthasarathy, Ankur Mallick, Gauri Joshi
arXiv:2607. 28848v1 Announce Type: cross Abstract: LLM serving systems are provisioned for peak load to meet strict latency targets, leaving substantial GPU compute idle whenever traffic falls below peak.
By Jiaxuan Chen, Jianshu She, Ye Yuan, Rajat Ghosh, Karan Gupta, Qirong Ho, Xue Liu, Oana Balmau