DraftExpert: Expansion-Aware Self-Speculative Decoding for End-Device MoE Inference
Researchers have introduced AngelSpec, a framework designed to improve speculative decoding efficiency for large language models. The method dynamically adapts drafting structures to different real-world workloads, addressing the inconsistency in performance often found when using standard autoregressive multi-token prediction.
Covered by 2 sources · 4 articles
- MMarkTechPost↗Michal SutterJul 30
- AarXiv CS.AI↗Dengke HanJul 28
- AarXiv CS.AI↗Zheng Wang, Zhifan Ye, Qi Cheng, Yonggan Fu, Ziyan Wang, Feng Zhu, Haozhe Zhao, Jan Kautz, Pavlo Molchanov, Humphrey Shi, Minjia ZhangJul 28
- AarXiv CS.AI↗Hong Liu, Rui Cen, Junhan Shi, Guangshuo Qin, Jiebin Zhang, Tianyu Liu, Runzhi Fan, Guoliang Zhao, Ruobing Xie, Kai Zhang, Song Liu, Guanghua Yu, Jianchen ZhuJul 29