arXiv:2608. 06557v1 Announce Type: cross Abstract: The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges to long, compute-heavy requests.
By Muhammad Adnan, Rohan Mahapatra, Prashant J. Nair, Daniel Berger, Pantea Zardoshti, Rodrigo Fonseca, Esha Choukse
Crossflow introduces an elastic boundary for prefilling and decoding in large language model serving, allowing decode nodes to publish short‑lived leases that limit prefilling resources and output projections. By adapting to dynamic phase demand, Crossflow improves token throughput by 16.2‑17.4% on average and up to 43.4% under high load, while consistently reducing mean time‑to‑first‑token. The approach eliminates the inefficiencies of static partitioning, which can leave 17% of cluster capacity idle or cause queueing and lost throughput.
By Yi Xu, Ehsan K. Ardestani, Wenyin Fu, Martin Schatz, Krishna Malladi, Zhan Shu, Adnan Aziz, Shobhit Kanaujia, Ajit Mathews, Chunqiang Tang
TOPAS is a Task‑Oriented Prefix‑Aware Scheduler designed for multi‑agent large language model serving. It jointly decides which agent prefixes to retain in a shared key‑value cache and which requests to schedule, balancing the reduction of each task’s longest remaining service path against the benefit of downstream prefix reuse while accounting for movement and preemption costs. Experiments on synthetic DAGs and MetaGPT software‑development workflows show that TOPAS can reduce mean and p99 job completion times by up to 39.8%/49.4% and 22.0%/26.6% respectively compared to the best baselines.
By Hongqiu Ni, Han Tian, Chi Zhang, Guopeng Li, Haisheng Tan
arXiv:2607. 17181v1 Announce Type: cross Abstract: Serverless multi-model LLM systems multiplex popularity-skewed model catalogs over shared GPU pools, yet typically schedule each request independently.
By Utopia Meng, Unicornt Zhao, Derek Li, Goalen Gao, Frank Du
arXiv:2609.37626v1 Announce Type: cross
Abstract: No single way of parallelizing attention serves large language models well under all loads. Low concurrency favors tensor parallelism, many independe...
By Chuan Liu, Shuoming Zhang, Zhicheng Li, Qianqi Sun, Ruiyuan Xu, Qiuchu Yu, Xiyu Shi, Huimin Cui, Jiacheng Zhao
arXiv:2607. 02043v1 Announce Type: cross Abstract: Disaggregated LLM serving runs prefill and decode on separate GPU pools to keep the two phases from interfering.
By Shrikara Arun, Anjaly Parayil, Srikant Bharadwaj, Renee St. Amant, Victor R\"uhle