Holistic Scaling Laws for Optimal Mixture-of-Experts Architecture Optimization

Abstract:Scaling laws govern macroscopic resource allocation for LLMs, yet precise architectural configurations for Mixture-of-Experts (MoE) models remain guided by heuristics. Existing MoE scaling studies either incorporate MoE-specific variables into scaling formulas, causing fitted coefficients to grow rapidly without proportional experimental support, or fix all non-MoE factors, implicitly assuming global architecture does not influence local MoE scaling. We propose a reusable framework for holistic MoE architectural optimization. We first show that MoE architectures decouple FLOPs per token ($M$), active parameters ($N_a$), and total parameters ($N$), which serve as distinct proxies for computation, weight access, and parameter storage. We therefore establish a joint constraint triad of $M$, active parameters ($N_a$), and total parameters ($N$) for rigorous MoE characterization. To tame the resulting $O(n^{16})$ search space, we employ mathematical decoupling: structural constraints and a rank-preserving property of the hidden dimension factorize the optimization into an $O(n^3) + O(n^2)$ two-phase search. Through extensive validation across 670+ MoE models spanning $10^{18}$ to $3 \times 10^{20}$ FLOPs, we derive globally applicable scaling laws mapping any compute budget to its optimal architecture. A key finding is that the near-optimal configuration band widens with scale, providing a quantitative basis for trade-offs between scaling law recommendations and engineering constraints. Our work delivers actionable blueprints for optimal MoE design under arbitrary compute budgets.

Camera-ready version accepted at NeurIPS 2026. 43 pages

Machine Learning (cs.LG)

arXiv:2603.21862 [cs.LG]

(or arXiv:2603.21862v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2603.21862 Focus to learn more arXiv-issued DOI via DataCite

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