Sparse Models vs. Dense Models: Efficiency Trade-offs in Foundation Models

Abstract

As AI foundation models scale to billions of parameters, the dichotomy between sparse and dense architectures has grown fundamental to both research and deployment. Dense models, typified by classical transformer-based networks, attain high accuracy but at significant computational, memory, and energy costs. In contrast, sparse models, including static/dynamic pruning and Mixture-of-Experts (MoE) activate a subset of parameters, reducing computational overhead and enabling expansion of model capacity with near-constant inference cost. This paper conducts a state-of-the-art review and empirical comparison of sparse versus dense foundation models, including optimization strategies and hardware-aware efficiency. Drawing upon 20+ peer-reviewed sources and recent empirical benchmarks, it demonstrates that recent advances in sparse models achieve comparable or superior efficiency and generalization on language and vision benchmarks. It provides detailed methodological pipelines, LaTeX math, clean Python code, real dataset descriptions, and professional graphs comparing key metrics. The analysis also confronts societal, ethical, and interpretability consequences of increased sparsity. Finally, it recommends directions for robust, reproducible, and scalable model deployment in academic and enterprise settings.