Scaling the capacity of language models has consistently proven to be a reliable approach for
improving performance and unlocking new capabilities. Capacity can be primarily defined by
two dimensions: the number of model parameters and the compute per example. While scaling
typically involves increasing both, the precise interplay between these factors and their combined contribution to overall capacity remains not fully understood. We explore this relationship
in the context of sparse Mixture-of-Experts (MoEs) , which allow scaling the number of parameters without proportionally increasing…
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Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
