EAGLE resolves feature-level uncertainty in speculative sampling via one-step token advancement, delivering 2.7x-3.5x speedup on LLaMA2-Chat 70B and doubled throughput across multiple model families and tasks.
Speed: Speculative pipelined execution for efficient decoding, 2024
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
LogitSpec accelerates retrieval-based speculative decoding by speculating the next-next token from the last logit and retrieving relevant references for both next and next-next tokens, reporting up to 2.61x speedup and 3.28 mean accepted tokens.
citing papers explorer
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EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
EAGLE resolves feature-level uncertainty in speculative sampling via one-step token advancement, delivering 2.7x-3.5x speedup on LLaMA2-Chat 70B and doubled throughput across multiple model families and tasks.
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LogitSpec: Accelerating Retrieval-based Speculative Decoding via Next Next Token Speculation
LogitSpec accelerates retrieval-based speculative decoding by speculating the next-next token from the last logit and retrieving relevant references for both next and next-next tokens, reporting up to 2.61x speedup and 3.28 mean accepted tokens.