llama.cpp’s probabilistic drafting accepts more tokens per pass
llama.cpp now offers an opt-in probabilistic drafting path that can make multi-token decoding faster by letting more draft tokens survive verification, so users can trade the default greedy behavior for higher acceptance. It uses rejection sampling on the CPU: a draft token is accepted with probability based on the target and draft distributions, rather than only when their top picks match. Reported acceptance-length gains range from 2.7% to 13.4%, depending on the model, drafter, and temperature.