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We present a novel inference scheme, self-speculative decoding, foraccelerating Large Language Models (LLMs) without the need for an auxiliarymodel. This approach is characterized by a two-stage process: drafting andverification. The drafting stage generates draft tokens at a slightly lowerquality but more quickly, which is achieved by selectively skipping certainintermediate layers during drafting Subsequently, the verification stageemploys the original LLM to validate those draft output tokens in one forwardpass. This process ensures the final output remains identical to that producedby the unaltered LLM, thereby maintaining output quality. The proposed methodrequires no additional neural network training and no extra memory footprint,making it a plug-and-play and cost-effective solution for inferenceacceleration. Benchmarks with LLaMA-2 and its fine-tuned models demonstrated aspeedup up to 1.73$\times$.

Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding