AI Model Optimization

Why Inference in Large Models Becomes Decomposable After Training

In contemporary large-scale AI models, inference is typically carried out by operating on full parameter matrices. As model size continues to increase, this paradigm leads to inference cost and system complexity that scale in an unsustainable manner. The root cause does not lie in limitations of model capacity or representational form. Rather, post-training inference systems have long been treated as monolithic operators, while internal structures formed during training are never explicitly identified. Based on an analysis of neural network learning dynamics, we show that gradient update events in large models are highly localized and selective in parameter space. After training, parameter matrices commonly contain a substantial fraction of parameter components that receive no effective sample support: the corresponding dependencies fail to accumulate stable gradient updates and remain statistically indistinguishable, at the distributional level, from their initialization distribution. Consequently, the post-training inference system is structurally non-uniform and inherently decomposable.