arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.
By Cyril Shih-Huan Hsu, Wig Yuan-Cheng Cheng, Chrysa Papagianni
arXiv:2609.08307v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires bala...
By Maysam Khatib, Moysis Symeonides, Demetris Trihinas, George Pallis, Marios D. Dikaiakos
arXiv:2608. 15127v1 Announce Type: cross Abstract: Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and persistent state.
By Chaokun Chang, Yukun Zhou, Kaihua Fu, Dakai An, Tianyu Feng, Hanfeng Lu, Sheng Yao, Pu Guo, Yinghao Yu, Yizhou Shan, Bo Li, Binhang Yuan, Wei Wang
arXiv:2511. 07885v5 Announce Type: replace-cross Abstract: Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure.
By Jon Saad-Falcon, Avanika Narayan, Hakki Orhun Akengin, J. Wes Griffin, Herumb Shandilya, Adrian Gamarra Lafuente, Medhya Goel, Rebecca Joseph, Shlok Natarajan, Etash Kumar Guha, Shang Zhu, Ben Athiwaratkun, John Hennessy, Azalia Mirhoseini, Christopher R\'e
MAPS is a Memory-Aware Predictive Scheduling framework designed for disaggregated large language model (LLM) serving. It uses device-assisted speculative output length prediction and uncertainty-aware calibration to establish safe output-length upper bounds, which inform a hierarchical global-local scheduling strategy that reduces queue buildup and head-of-line blocking. Experiments on real-world workloads and two LLMs demonstrate that MAPS lowers average end-to-end latency by 42.6% and tail latency by up to 84.8% compared to three state-of-the-art systems.
By Tiancheng Zhang, Yulin Chen, Yunfeng Zhao, Shaoyuan Huang, Cheng Zhang, Xiaofei Wang
arXiv:2607. 14661v1 Announce Type: new Abstract: Deploying large language models (LLMs) as personal assistants on mobile devices demands privacy, low latency, and offline availability, yet the computational cost of giant models clashes with strict edge-hardware budgets.
By Zhihan Jiang, Meng Li, Shenghao Liu, Keran Li, Ruiben Zhou, Xianjun Deng, Shuai Wang, Haipeng Dai