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cs.DC2025★ 2 cited
HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs
Yongji Wu, Xueshen Liu, Shuowei Jin +6
The Mixture-of-Experts (MoE) architecture has become increasingly popular as a method to scale up large language models (LLMs). To save costs, heterogeneity-aware training solution…
cs.DC2025
AIBrix: Towards Scalable, Cost-Effective Large Language Model Inference Infrastructure
The AIBrix Team, Jiaxin Shan, Varun Gupta +24
We introduce AIBrix, a cloud-native, open-source framework designed to optimize and simplify large-scale LLM deployment in cloud environments. Unlike traditional cloud-native stack…