Speculative decoding (SD) has emerged as a promising approach to accelerate inference in large language models (LLMs). This method drafts potential future tokens by leveraging a smaller model, while these tokens are concurrently verified by the target LLM, ensuring only outputs aligned with the target LLM’s predictions are accepted. However, the limited capacity of individual drafters often hinders their effectiveness across diverse tasks. In this paper, we introduce a unified framework that incorporates multiple drafters into the speculative decoding process to address this limitation. Our approach employs multi-armed bandit sampling to dynamically allocate computational resources and optimize inference across various drafters, thereby improving overall generation performance. Through extensive experiments, we demonstrate that our unified framework achieves superior results compared to traditional single-drafter approaches.