In a recent study, researchers at Meta, Ecole des Ponts ParisTech and Université Paris-Saclay suggest improving the accuracy and speed of AI large language models (LLMs) by making them predict ...

Enterprises expanding AI deployments are hitting an invisible performance wall. The culprit? Static speculators that can't keep up with shifting workloads. Speculators are smaller AI models that work ...

With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.

For years, every large language model – GPT, Gemini, Claude, or Llama – has been built on the same underlying principle: predict the next token. That simple loop of going one token at a time is the ...