What is Sparse Model?
TL;DR
An efficient AI model design that deactivates most parameters and computes only the necessary parts for each input.
Sparse Model: Definition & Explanation
Sparse models are an efficient neural network design approach that keeps most parameters or computations inactive (zero) and activates only the necessary portions based on input. The most prominent approach is Mixture of Experts (MoE), adopted in large models like GPT-4 and Mixtral. In MoE, only a small number of 'expert' sub-networks are activated for each input, keeping actual computation manageable despite massive total parameter counts. Post-training sparsification through pruning is also widely researched, contributing to more efficient AI execution on edge devices. It is a critical technology for balancing inference cost reduction with model performance.