SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and
Scalable Large Mixture-of-Experts Models

SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models

29 October 2023

Yiran Chen

Papers citing "SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models"

7 / 7 papers shown

Title
DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference Yujie Zhang Shivam Aggarwal T. Mitra MoE 67 0 0 16 Dec 2024
SCOTT: Self-Consistent Chain-of-Thought Distillation Jamie Yap Zhengyang Wang Zheng Li K. Lynch Bing Yin Xiang Ren LRM 57 91 0 03 May 2023
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions Minghao Wu Abdul Waheed Chiyu Zhang Muhammad Abdul-Mageed Alham Fikri Aji ALM 127 115 0 27 Apr 2023
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference Haiyang Huang Newsha Ardalani Anna Y. Sun Liu Ke Hsien-Hsin S. Lee Anjali Sridhar Shruti Bhosale Carole-Jean Wu Benjamin C. Lee MoE 65 21 0 10 Mar 2023
Tutel: Adaptive Mixture-of-Experts at Scale Changho Hwang Wei Cui Yifan Xiong Ziyue Yang Ze Liu ... Joe Chau Peng Cheng Fan Yang Mao Yang Y. Xiong MoE 92 107 0 07 Jun 2022
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,424 0 23 Jan 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 294 6,927 0 20 Apr 2018