Expert Parallelism in Large Language Models: How All-to-All Token Dispatch, Capacity Factors, and Parallel Folding Scale MoE Architectures
Expert Parallelism in Large Language Models: How All-to-All Token Dispatch, Capacity Factors, and Parallel Folding Scale MoE Architectures Scaling dense Large Language Models (LLMs) requires activating every parameter in the network for every token in a sequence. While techniques like Tensor Parallelism, Pipeline Parallelism, and Fully Sharded Data Parallelism distribute billions of dense parameters across clusters, computational cost scales linearly with parameter count. Mixture-of-Experts (Mo
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