Speculative Decoding (SD) utilizes an efficient draft model to generate multiple tokens, which are subsequently verified in parallel by a target model. This approach has shown significant potential for accelerating inference in large language models (LLMs), with performance heavily reliant on the hyperparameter K—the window size. However, previous methods often depend on simple heuristics to select K or dynamically adjust the window size, which may necessitate additional training or careful resource management to avoid competition.To address these challenges, we propose Hierarchical Speculative Decoding with Dynamic Window (HSDDW), a straightforward framework that eliminates the need for additional training. Specifically, we introduce a self-verify mechanism that enables the draft model to autonomously decide when to stop generating tokens. Additionally, by integrating a hierarchical structure that leverages the capabilities of models of different sizes, we significantly enhance the overall speed of the system.HSDDW demonstrates competitive performance across four datasets, achieving notable speedups of 2.91× on MT-Bench and 2.99× on Alpaca, outperforming existing state-of-the-art methods.