What is Speculative Decoding?
Speculative decoding has emerged as a noteworthy technique in the field of natural language processing, particularly for optimizing inference in large language models (LLMs). This method employs a two-tiered approach where a small and speedy 'draft' model generates several potential future tokens. These tokens are then concurrently verified by a larger, more sophisticated 'target' model.
The main advantage of this process is its ability to significantly accelerate the token generation phase, allowing multiple tokens to be processed at each step. This not only enhances efficiency but also maintains the quality of the output, making it a valuable tool for developers and researchers working with LLMs.
Recently, SGLang, a prominent framework for managing LLMs in conjunction with vLLM, highlighted their advancements in achieving state-of-the-art latency for LLM inference. Their blog post details how they leverage Modal and Z.ai's DFlash speculative decoding models to optimize performance further. For those interested in exploring this technique, additional resources and papers referencing the original introduction of speculative decoding can be found on Papers with Code.
For further insights, check out SGLang's blog here.
