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arXiv · 2504.08838

SD$^2$: Self-Distilled Sparse Drafters

Mike Lasby·Nish Sinnadurai·Valavan Manohararajah·Sean Lie·Yani Ioannou·Vithursan Thangarasa
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Abstract

Speculative decoding is a powerful technique for reducing the latency of Large Language Models (LLMs), offering a fault-tolerant framework that enables the use of highly compressed draft models. In this work, we introduce Self-Distilled Sparse Drafters (SD$^2$), a novel methodology that leverages self-data distillation and fine-grained weight sparsity to produce highly efficient and well-aligned draft models. SD$^2$ systematically enhances draft token acceptance rates while significantly reducing Multiply-Accumulate operations (MACs), even in the Universal Assisted Generation (UAG) setting, where draft and target models originate from different model families. On a Llama-3.1-70B target model, SD$^2$ provides a 1.59$\times$ higher Mean Accepted Length (MAL) compared to layer-pruned draft models and reduces MACs by over 43.87% with a 8.36% reduction in MAL compared to a dense draft models. Our 1.5B and 3B unstructured sparse drafters outperform both dense and layer-pruned models in terms of end-to-end latency improvements; highlighting the potential of sparsity-aware fine-tuning and compression strategies to improve LLM inference efficiency while maintaining alignment with target models.

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Mike Lasby, Nish Sinnadurai, Valavan Manohararajah, Sean Lie, Yani Ioannou, Vithursan Thangarasa. 2025-05-31. SD$^2$: Self-Distilled Sparse Drafters. https://arxiv.org/abs/2504.08838

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