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This paper introduces SpecInfer, a system that accelerates generative largelanguage model (LLM) serving with tree-based speculative inference andverification. The key idea behind SpecInfer is leveraging small speculativemodels to predict the LLM's outputs; the predictions are organized as a tokentree, whose nodes each represent a candidate token sequence. The correctness ofall candidate token sequences represented by a token tree is verified againstthe LLM in parallel using a novel tree-based parallel decoding mechanism.SpecInfer uses an LLM as a token tree verifier instead of an incrementaldecoder, which significantly reduces the end-to-end latency and computationalrequirement for serving generative LLMs while provably preserving modelquality. Our evaluation shows that SpecInfer outperforms existing LLM servingsystems by 1.5-2.8x for distributed LLM inference and by 2.6-3.5x foroffloading-based LLM inference, while preserving the same generativeperformance. SpecInfer is publicly available athttps://github.com/flexflow/FlexFlow/

SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification