Accelerating Dense LLMs via L0-regularized Mixture-of-Experts
Large language models (LLMs) achieve strong performance but suffer from slow and costly inference. Existing acceleration methods often lead to noticeable performance degradation, while Mixture-of-Experts (MoE) models require extensive computational resources. In this paper, we propose L0-MoE, a lightweight MoE approach using L0-regularization to accelerate dense LLMs nearly without performance…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
