Accelerating LLM Inference with Staged Speculative Decoding

• URL: http://arxiv.org/abs/2308.04623v1
• Date: Tue, 8 Aug 2023 23:29:55 GMT
• Title: Accelerating LLM Inference with Staged Speculative Decoding
• Authors: Benjamin Spector and Chris Re
• Abstract summary: We propose a novel algorithm, staged speculative decoding, to accelerate LLM inference in small-batch, on-device scenarios. We reduce single-batch decoding latency by 3.16x with a 762M parameter GPT-2-L model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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