Computer Science > Artificial Intelligence
arXiv:2308.04623 (cs)
[Submitted on 8 Aug 2023]
Title:Accelerating LLM Inference with Staged Speculative Decoding
View a PDF of the paper titled Accelerating LLM Inference with Staged Speculative Decoding, by Benjamin Spector and Chris Re
View PDFAbstract: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.
| Comments: | Published at ES-FOMO at ICML 2023 |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2308.04623 [cs.AI] |
| (orarXiv:2308.04623v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2308.04623 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Accelerating LLM Inference with Staged Speculative Decoding, by Benjamin Spector and Chris Re
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