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arXiv · 2409.00142

Dynamic Depth Decoding: Faster Speculative Decoding for LLMs

Oscar Brown·Zhengjie Wang·Andrea Do·Nikhil Mathew·Cheng Yu
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Abstract

The acceleration of Large Language Models (LLMs) with speculative decoding provides a significant runtime improvement without any loss of accuracy. Currently, EAGLE-2 is the state-of-the-art speculative decoding method, improving on EAGLE with a dynamic draft tree. We introduce Dynamic Depth Decoding (DDD), which optimises EAGLE-2's tree drafting method using a dynamic depth. This extends the average speedup that EAGLE-2 achieves over EAGLE by $44\%$, giving DDD an average speedup of $3.16$x.

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Oscar Brown, Zhengjie Wang, Andrea Do, Nikhil Mathew, Cheng Yu. 2024-08-30. Dynamic Depth Decoding: Faster Speculative Decoding for LLMs. https://arxiv.org/abs/2409.00142

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