> ... speculative decoding methods ... incurs extra memory cost during inference time. Any detail on this? For speculative decoding you need a smaller model to

> ... speculative decoding methods ... incurs extra memory cost during inference time. Any detail on this? For speculative decoding you need a smaller model to generate "branches" which are fast but maybe inaccurate and verify these branches later with a larger model. However, only memory equivalent to a single token is needed for speculative decoding, and tokens in other branches are simply masked out during inference. With a context size of 1000 and ~30 branches for 5 tokens, the memory overhead would be 3% which is negligible. If your context size is much smaller compared to the number of branches - would someone who use a generative LLM with a context window of just 50 tokens care about generation speed? Also, speculative decoding techniques are not restricted to greedy sampling - it's expected to behave exactly the same as the original model and sample with the expected probabilities. Most literature on speculative decoding already reports 2.6x-3.5x speedup. The blog post here reports 2.4x-3.4x generation speed - which isn't that much of an upgrade? While I mentioned speculative decoding above and Medusa2 and Eagle seems to be the techniques that the author compares against, the core problem remains: whatever method you use to predict tokens ahead of time, there is a specific point where the previous tokens are absolutely needed before predicting the next token. It doesn't depend on what your model is or what your techniques are, it's just about what is mathematically achievable. How can you predict 5 tokens at once if the probability distribution of the 5th next token depends heavily on the previous 4 tokens? Speculative decoding, Jacobi decoding, multi-token parallel decoding, whatever. If only greedy sampling is supported for this, then I wonder what are the advantages of this method, not to mention that other techniques already achieve the expected speedup. Comparing greedy sampling speedups to random sampling speedups is comparing apples to oranges, and I doubt if the speedup described by the method would remain after this method is adapted to random sampling (due to the core problem mentioned above).