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
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
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
arXiv:2607. 19349v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge.
By Tiancheng Zhang, Shaoyuan Huang, Mingyuan Wang, Yunfeng Zhao, Xiaofei Wang, Wenyu Wang
arXiv:2608. 16336v1 Announce Type: cross Abstract: Modern LLM serving deployments must simultaneously satisfy heterogeneous service-level objectives (SLOs) across a diverse population of user tiers, ranging from latency-critical API calls to background batch processing.
By Anders Vestrum, Arya Raeesi, Hanna Roed
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