What is Speculative Decoding? (trending on paperswithco.de) [R]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
| A method that is currently trending on Papers with Code is Speculative Decoding. Speculative decoding is an inference optimization technique that uses a fast, small "draft" model to quickly propose several future tokens, which are then verified in parallel by a larger, slower "target" model. This process significantly speeds up token generation for large language models (LLMs) by allowing multiple tokens per step without sacrificing output quality. SGLang, one of the most popular frameworks for running LLMs alongside vLLM, just released a blog post detailing how they achieve state-of-the-art latencies for LLM inference serving using Modal and Z.ai's DFlash speculative decoding models. Learn more at https://paperswithcode.co/methods/speculative-decoding. You can also find all the papers that cite the original paper that introduced this technique. SGLang's blog: https://www.lmsys.org/blog/2026-06-15-next-generation-speculative-decoding-dflash-v2/ Let me know which other methods I should add! Cheers, [link] [comments] |
More from r/MachineLearning
-
Loss functions in Instance Representation Learning [R]
Jun 29
-
Price elasticity model [R]
Jun 29
-
Rejected MICCAI paper: workshop -> journal/conference or directly journal/conference [R]
Jun 29
-
I built a demo agricultural planning system with an AI advisor for small-scale farmers in Nicaragua using NASA data [p]
Jun 29
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.