InfoWorld

Multi-token prediction technique triples LLM inference speed without auxiliary draft models

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 baked 3x inference speedups directly into LLM weights — without speculative decoding

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 ...
9to5Mac

Apple taught an LLM to predict tokens up to 5x faster in math and coding tasks

A new research paper from Apple details a technique that speeds up large language model responses, while preserving output quality. Here are the details. Traditionally, LLMs generate text one token at ...
Arabian Post on MSN

Building Chatbots with Falcon LLM Without API Tokens

The advent of open-source large language models has democratized chatbot development, enabling developers to create sophisticated conversational agents without relying on costly API tokens. One such ...