新研究助力移动设备上的 LLM 更快、更高效

报道来源 [2]

1. arXiv cs.LG TIER_1 English(EN) · Hanxian Huang, Igor Fedorov, Andrey Gromov, Bernard Beckerman, Naveen Suda, David Eriksson, Maximilian Balandat, Rylan Conway, Patrick Huber, Chinnadhurai Sankar, Ayushi Dalmia, Zechun Liu, Lemeng Wu, Tarek Elgamal, Adithya Sagar, Vikas Chandra, Raghurama · 2026-04-29 04:00

   MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment

   arXiv:2603.15954v2 Announce Type: replace Abstract: Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The most useful OD-LLMs produce near-real-time responses and exhibit broad hardware…

2. arXiv cs.CL TIER_1 English(EN) · Sravanth Kodavanti, Sowmya Vajrala, Srinivas Miriyala, Utsav Tiwari, Uttam Kumar, Utkarsh Kumar Mahawar, Achal Pratap Singh, Arya D, Narendra Mutyala, Vikram Nelvoy Rajendiran, Sharan Kumar Allur, Euntaik Lee, Dohyoung Kim, HyeonSu Lee, Gyusung Cho, JungB · 2026-04-27 04:00

   Unlocking the Edge deployment and ondevice acceleration of multi-LoRA enabled one-for-all foundational LLM

   arXiv:2604.18655v2 Announce Type: replace-cross Abstract: Deploying large language models (LLMs) on smartphones poses significant engineering challenges due to stringent constraints on memory, latency, and runtime flexibility. In this work, we present a hardware-aware framework f…