S2D: Sorted Speculative Decoding For More Efficient Deployment of Large Language Models

Deployment of autoregressive large language models (LLMs) is costly, and as these models increase in size, the associated costs will become even more considerable. Consequently, different methods have been proposed to accelerate the token generation process and reduce costs. Speculative decoding (SD) is among the most promising approaches to speed up the LLM decoding process by verifying multiple tokens in parallel and using an auxiliary smaller draft model to generate the possible tokens. In SD, usually one draft model is used to serve a specific target model; however, in practice, LLMs are diverse, and we might need to deal with many target models or more than one target model simultaneously. In this scenario, it is not clear which draft model should be used for which target model, and searching among different draft models, or training customized draft models, can further increase deployment costs. In this paper, we first introduce a novel multi-target scenario for deployment of draft models for faster inference. Then, we present a novel more efficient sorted speculative decoding mechanism that outperforms regular baselines in multi-target setting. We evaluated our method on Spec-Bench in different settings including base models such as Vicuna 7B, 13B, and LLama Chat 70B. Our results suggest that our draft models perform better than baselines for multiple target models at the same time.