Accelerating LLM Inference with Staged Speculative Decoding

Benjamin Spector, Chris Re

Accelerating LLM Inference with Staged Speculative Decoding: 26 upvotes on Hugging Face Daily Papers, #3 of 4 papers on 2023-08-10. Day-by-day upvote history.

Recent advances with large language models (LLM) illustrate their diverse capabilities. We propose a novel algorithm, staged speculative decoding, to accelerate LLM inference in small-batch, on-device scenarios. We address the low arithmetic intensity of small-batch inference by improving upon previous work in speculative decoding. First, we restructure the speculative batch as a tree, which reduces generation costs and increases the expected tokens per batch. Second, we add a second stage of speculative decoding. Taken together, we reduce single-batch decoding latency by 3.16x with a 762M parameter GPT-2-L model while perfectly preserving output quality.

Paper page on Hugging Face · arXiv

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