With the advent of large-scale language models (LLMs), various optimization techniques have been proposed to enable efficient inference. Among these, methods that aggressively exploit output activation sparsity have attracted significant attention, which leverage ReLU-fied LLMs and skip the entire memory accesses as well as the computation for the output element if it was predicted as sparse. Achieving fast and accurate prediction of output activation sparsity is crucial to enhancing inference efficiency. However, in practice, phenomena such as activation outliers and hot and cold neurons, which significantly affect the exploitation of sparsity during LLM inference, have either been addressed individually or not structurally integrated in existing work.