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 the FLOPs per example. We investigate how varying
the sparsity level, i.e., the fraction of inactive parameters, impacts model’s performance during
pretraining and downstream few-shot evaluation. We find that under different constraints (e.g.,
parameter size and total training compute), there is an optimal level of sparsity that improves
both training efficiency and model performance. These results provide a better understanding
of the impact of sparsity in scaling laws for MoEs and complement existing works in this area,
offering insights for designing more efficient architectures.