CARD: A Cache-Assisted Parallel Speculative Decoding Framework via Query-and-Correct Paradigm for Accelerating LLM Inference

• URL: http://arxiv.org/abs/2508.04462v2
• Date: Fri, 19 Sep 2025 06:20:14 GMT
• Title: CARD: A Cache-Assisted Parallel Speculative Decoding Framework via Query-and-Correct Paradigm for Accelerating LLM Inference
• Authors: Enyu Zhou, Kai Sheng, Hao Chen, Xin He,
• Abstract summary: We propose a speculative decoding framework called CARD, which employs a novel query-and-correct paradigm.<n>Our approach decouples drafting from verification, effectively leveraging the draft model's efficiency without additional fine-tuning.<n> CARD significantly outperforms existing state-of-the-art methods, achieving up to a 4.83x acceleration over vanilla autoregressive decoding.
• Score: 14.527697328189362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speculative decoding (SD), where a draft model provides multiple candidate tokens for the target model to verify in parallel, has demonstrated significant potential for accelerating LLM inference. Yet, existing SD approaches adhere to a strict draft-then-verify paradigm, enforcing a sequential process that hampers performance and constrains the draft model's capacity. Moreover, rejecting a token in the candidate sequence invalidates all subsequent tokens, leading to wasted computation during drafting. To overcome these limitations, we propose a cache-assisted parallel speculative decoding framework called CARD, which employs a novel query-and-correct paradigm. Our approach decouples drafting from verification: the draft model populates a shared cache with candidate tokens, while the target model concurrently refines the draft's trajectory. This enables inference at near-draft-speed, effectively leveraging the draft model's efficiency without additional fine-tuning. Experimental results show that CARD significantly outperforms existing state-of-the-art methods, achieving up to a 4.83x acceleration over vanilla autoregressive decoding, with no fine-tuning required for either models.

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