llms.blog
MoE2 articles

MoE

Articles

  • Serving Mixture-of-Experts Models in Production: Architecture, Distributed Parallelism, All-to-All Bottlenecks, and Serving Economics

    Serving Mixture-of-Experts Models in Production: Architecture, Distributed Parallelism, All-to-All Bottlenecks, and Serving Economics Mixture-of-Experts (MoE) architectures have become the standard structural paradigm for frontier open-weight and proprietary large language models. Architectures such as DeepSeek-V3, Mixtral 8x22B, and GLM-5 deliver frontier-grade reasoning and generation by activating only a fraction of their total parameter count on any given token. For example, DeepSeek-V3 rou

    1 min

  • How Mixture of Experts Works: Sparse Gating, Token Routing, and Load Balancing

    Large language models face a fundamental scaling trade-off: as parameter counts grow to absorb more world knowledge, the floating-point operations (FLOPs) required per token increase proportionally. In a standard dense Transformer, every single parameter participates in processing every incoming token. Mixture of Experts (MoE) decouples total model capacity from per-token compute cost. By replacing dense feed-forward layers with a dynamic pool of specialized sub-networks ("experts") and routing

    1 min