A deep-dive tutorial on implementing the Mixture of Experts (MoE) layer in DeepSeek-V3 from scratch. Covers the mathematical foundation of MoE routing, SwiGLU activation, shared expert design, and DeepSeek's auxiliary-loss-free load balancing via dynamic bias updates. Includes full PyTorch implementation with expert routing,
Table of contents
DeepSeek-V3 from Scratch: Mixture of Experts (MoE)The Scaling Challenge in Neural NetworksMixture of Experts (MoE): Mathematical Foundation and Routing MechanismSwiGLU Activation in DeepSeek-V3: Improving MoE Non-LinearityShared Expert in DeepSeek-V3: Universal Processing in MoE LayersAuxiliary-Loss-Free Load Balancing in DeepSeek-V3 MoESequence-Wise Load Balancing for Mixture of Experts ModelsExpert Specialization in MoE: Emergent Behavior in DeepSeek-V3Implementation: Building the DeepSeek-V3 MoE Layer from ScratchMoE Design Decisions in DeepSeek-V3: SwiGLU, Shared Experts, and RoutingMoE Computational and Memory Analysis in DeepSeek-V3MoE Expert Specialization in Practice: Real-World BehaviorTraining Dynamics of MoE: Load Balancing and Expert UtilizationMixture of Experts vs Related Techniques: Switch Transformers and Sparse ModelsSummarySort: