Reasoning large language models (LLMs) are designed to solve complex problems by breaking them down into a series of smaller ...

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.

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...

Training large language models is brutally expensive. It’s not just about having more GPUs; it’s about how efficiently you use them. And as models scale up, even small inefficiencies can turn into ...

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 ...

This figure shows an overview of SPECTRA and compares its functionality with other training-free state-of-the-art approaches across a range of applications. SPECTRA comprises two main modules, namely ...