MoE

What is Mixture of Experts (MoE) Architecture? In the rapidly evolving field of artificial intelligence, large-scale models continue to push the boundaries of performance. One breakthrough approach that has significantly improved the efficiency of such models is the Mixture of Experts (MoE) architecture. MoE enables massive scalability while keeping computational costs manageable, making it a key innovation in deep learning. 1. Understanding the MoE Architecture At its core, MoE is a sparse activation neural network that dynamically selects different subsets of parameters for each input. Unlike traditional dense neural networks where all neurons are activated for every input, MoE activates only a small portion of its network, leading to more efficient computation. ...

DeepSeek-V3: Groundbreaking Innovations in AI Models DeepSeek-V3, the latest open-source large language model, not only rivals proprietary models in performance but also introduces groundbreaking innovations across multiple technical areas. This article explores the key advancements of DeepSeek-V3 in architecture optimization, training efficiency, inference acceleration, reinforcement learning, and knowledge distillation. 1. Mixture of Experts (MoE) Architecture Optimization 1.1 DeepSeekMoE: Finer-Grained Expert Selection DeepSeek-V3 employs the DeepSeekMoE architecture, which introduces shared experts compared to traditional MoE (e.g., GShard), improving computational efficiency and reducing redundancy. ...