3 papers
cs.DC2026
Scalable Training of Mixture-of-Experts Models with Megatron Core
Zijie Yan, Hongxiao Bai, Xin Yao +42
Scaling Mixture-of-Experts (MoE) training introduces systems challenges absent in dense models. Because each token activates only a subset of experts, this sparsity allows total pa…
cs.CL2025
Upcycling Large Language Models into Mixture of Experts
Ethan He, Abhinav Khattar, Ryan Prenger +7
Upcycling pre-trained dense language models into sparse mixture-of-experts (MoE) models is an efficient approach to increase the model capacity of already trained models. However,…
cs.LG2024
Llama 3 Meets MoE: Efficient Upcycling
Aditya Vavre, Ethan He, Dennis Liu +4
Scaling large language models (LLMs) significantly improves performance but comes with prohibitive computational costs. Mixture-of-Experts (MoE) models offer an efficient alternati…