Fast Inference via Hierarchical Speculative Decoding

• URL: http://arxiv.org/abs/2510.19705v2
• Date: Thu, 23 Oct 2025 14:15:48 GMT
• Title: Fast Inference via Hierarchical Speculative Decoding
• Authors: Clara Mohri, Haim Kaplan, Tal Schuster, Yishay Mansour, Amir Globerson,
• Abstract summary: We introduce Hierarchical Speculative Decoding (HSD), an algorithm that stacks draft models into a hierarchy, where each model proposes tokens, and the next larger model verifies them in a single forward pass.<n>HSD gives up to 1.2x speed-up over the best single-draft baseline.
• Score: 65.40448210801763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer language models generate text autoregressively, making inference latency proportional to the number of tokens generated. Speculative decoding reduces this latency without sacrificing output quality, by leveraging a small draft model to propose tokens that the larger target model verifies in parallel. In practice, however, there may exist a set of potential draft models- ranging from faster but less inaccurate, to slower yet more reliable. We introduce Hierarchical Speculative Decoding (HSD), an algorithm that stacks these draft models into a hierarchy, where each model proposes tokens, and the next larger model verifies them in a single forward pass, until finally the target model verifies tokens. We derive an expression for the expected latency of any such hierarchy and show that selecting the latency-optimal hierarchy can be done in polynomial time. Empirically, HSD gives up to 1.2x speed-up over the best single-draft baseline, demonstrating the practicality of our algorithm in reducing generation latency beyond previous techniques.

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