CARD: Cache-Assisted Parallel Speculative Decoding for Efficient Large Language Model Inference

Abstract: Speculative decoding (SD), where an extra draft model first provides multiple draft tokens and the original target model then verifies these tokens in parallel, has shown great power for LLM inference acceleration. However, existing SD methods must adhere to the 'draft-then-verify' paradigm, which forces drafting and verification processes to execute sequentially during SD, resulting in inefficient inference performance and limiting the size of the draft model. Furthermore, once a single token in the candidate sequence is rejected during the drafting process, all subsequent candidate tokens must be discarded, leading to inefficient drafting. To address these challenges, we propose a cache-based parallel speculative decoding framework employing a 'query-and-correct' paradigm. Specifically, CARD decouples drafting and verification: the draft model generates candidate tokens to populate a shared cache, while the target model concurrently rectifies the draft model's generation direction. This effectively enables the target model to perform inference at speed approaching that of the draft model. Our approach achieves up to 4.83 speedup over vanilla decoding without requiring fine-tuning of either the draft or target models. Our code is available at https://github.com/hunzhizi/CARD.