CATS achieves up to 5.08x wall-clock speedup for LLM generation on edge devices via memory-matched cascaded tree speculation, outperforming prior methods by 1.45x with no quality loss.
OPT-Tree: Speculative decoding with adaptive draft tree structure
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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.
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CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration
CATS achieves up to 5.08x wall-clock speedup for LLM generation on edge devices via memory-matched cascaded tree speculation, outperforming prior methods by 1.45x with no quality loss.
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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.