In enterprise AI, the models that win won't be the largest. They'll be the ones that know exactly what they're built for.​​

Large language models can write essays, solve math problems, and generate computer code, but it’s not fully understood how they do it. Researchers can observe the billions of parameters inside these systems changing during training,

Learn how to run a 32B local LLM on a $599 Mac Mini using Ollama. This setup reduces cloud AI costs while maintaining strong inference performance.

Bigger has defined AI from day one. New data says task-specific small models beat frontier LLMs on accuracy, cost and speed — and save money.

Stories of runaway AI bills have been making some executives skittish about AI spending.

Since Wix.com Ltd. (Nasdaq: WIX) bought Base44 last year, the acquisition has become its main growth engine. In May alone, Base44 recorded annual recurring revenue (ARR) of about $150 million, compared with just several million when it was acquired.

Enterprises are turning to smaller language models to manage rising AI token costs and enhance return on investment effectively.

Designing high-performance catalysts is essential for cleaner energy technologies, but the behavior of multi-element modern catalyst materials is difficult to predict. In a new study, researchers at Tohoku University with international collaborators developed a collaborative framework that combines large language models with lab experiments to accelerate the discovery of high-entropy alloy catalysts for the oxygen reduction reaction,

When a standard large language model (LLM) is confronted with a problem, it tries to solve it by matching it to similar information it has seen before, and then give an answer based on those past patterns.