SlimSpec: Low-Rank Draft LM-Head for Accelerated Speculative Decoding

TL;DR

SlimSpec introduces a low-rank LM-head for draft models in speculative decoding, significantly accelerating inference while maintaining full vocabulary support and minimal pipeline changes.

Contribution

It proposes a low-rank parameterization of the draft LM-head that reduces computation without sacrificing vocabulary support, outperforming existing methods.

Findings

Achieves 4-5x acceleration over standard LM-heads.

Surpasses existing methods by up to 8-9% in end-to-end speedup.

Maintains competitive acceptance length across models and benchmarks.

Abstract

Speculative decoding speeds up autoregressive generation in Large Language Models (LLMs) through a two-step procedure, where a lightweight draft model proposes tokens which the target model then verifies in a single forward pass. Although the drafter network is small in modern architectures, its LM-head still performs projection to a large vocabulary, becoming one of the major computational bottlenecks. In prior work this issue has been predominantly addressed via static or dynamic vocabulary truncation. Yet mitigating the bottleneck, these methods bring in extra complexity, such as special vocabulary curation, sophisticated inference-time logic or modifications of the training setup. In this paper, we propose SlimSpec, a low-rank parameterization of the drafter's LM-head that compresses the inner representation rather than the output, preserving full vocabulary support. We evaluate our method with EAGLE-3 drafter across three target models and diverse benchmarks in both latency- and throughput-bound inference regimes. SlimSpec achieves $4\text{-}5\times$ acceleration over the standard LM-head architecture while maintaining a competitive acceptance length, surpassing existing methods by up to $8\text{-}9\%$ of the end-to-end speedup. Our method requires minimal adjustments of training and inference pipelines. Combined with the aforementioned speedup improvements, it makes SlimSpec a strong alternative across wide variety of draft LM-head architectures.