LeanStream introduces a speculate‑and‑refine streaming framework that enables efficient on‑device inference of large language models by progressively refining computation, loading, and cache‑retention priorities using partial GPU results. This approach allows fine‑grained overlap between GPU execution and storage I/O, avoiding the trade‑offs of existing systems that serialize execution or incur redundant weight fetches. Implemented on mobile and embedded platforms, LeanStream reduces memory usage by 4.8× to 7.5× compared to prior work while improving token generation throughput by 1.6× to 2.1×.
By Renyuan Liu (Richard), Yuyang Leng (Richard), Kaiyan Liu (Richard), Yuzhou Zhong (Richard), Shaohan Hu (Richard), Chun-Fu (Richard), Chen, Peijun Zhao, Heechul Yun, Shuochao Yao
arXiv:2609.13592v1 Announce Type: cross
Abstract: GPU memory bandwidth and capacity limit throughput in large language model (LLM) inference. The GPU memory system consists of a primary tier of high-...
By Anish Saxena, Jae Hyung Ju, Hritvik Taneja, Po-An Tsai, Aamer Jaleel, Christos Kozyrakis, Moinuddin Qureshi
arXiv:2606. 10493v1 Announce Type: cross Abstract: Local deployment of large Mixture-of-Experts (MoE) models falls short of the service quality achieved in cloud-scale environments, even under low-concurrency workloads.
By Wenxin Wang, Yule Hou, Yu Ji, Peng Qu, Youhui Zhang
arXiv:2609.06551v1 Announce Type: cross
Abstract: Mobile vendors and application developers increasingly deploy LLMs on smartphones for diverse prefill-only services. Yet current systems rely mainly...
By Junming Zhang, Zhenzhe Zheng, Fan Wu, Xiaoyao Huang, Jie Wu
arXiv:2609.01338v1 Announce Type: cross
Abstract: On-device mobile Large Language Model (LLM) inference is gaining significant attention. However, mobile devices operate in highly dynamic multitaskin...
By Hongseung Yu, Minsung Kim, Jongseok Park, Kyunghan Lee
arXiv:2607. 05475v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) on mobile devices enhances privacy and reduces latency, but is severely bottlenecked by hardware inefficiency.
By Guanyu Cai, Ruiming Tian, Lang Yang, Zhouhong Ren, Jinliang Yuan, Lingkun Li, Jiliang Wang