Eagle 3.1: Collaboration Between the EAGLE Team, vLLM Team, and TorchSpec Team | Hacker News

EAGLE 3.1 is a speculative decoding method from the EAGLE, vLLM, and TorchSpec teams that speeds up LLM token generation using a draft model plus main-model verification.

It aims to improve inference without changing output quality, with particular promise for local and hosted models at low concurrency and long context lengths.

Commenters say it can work well on consumer hardware, but gains depend heavily on model quality, workload, and acceptance rate.

A key limitation is that it may slow down in worst cases, with attention drift cited as an important weakness.