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SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
v1v2 (latest)

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

Zhixu Du
Shiyu Li
Yuhao Wu
Xiangyu Jiang
Jingwei Sun
Qilin Zheng
Yongkai Wu
Ang Li
Hai Helen Li
Yiran Chen
    MoE
ArXiv (abs)PDFHTMLGithub (17★)

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

3 / 3 papers shown
Title
DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference
DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference
Yujie Zhang
Shivam Aggarwal
T. Mitra
MoE
167
1
0
16 Dec 2024
Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference
Andrii Skliar
T. V. Rozendaal
Romain Lepert
Todor Boinovski
M. V. Baalen
Markus Nagel
Paul N. Whatmough
B. Bejnordi
MoE
174
2
0
27 Nov 2024
Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts
Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts
Weilin Cai
Juyong Jiang
Le Qin
Junwei Cui
Sunghun Kim
Jiayi Huang
185
10
0
07 Apr 2024

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