Meet the IBM researchers trying to make LLMs smarter

Large language models still dominate AI leaderboards, but a new class of lighter weight models are closing the gap. IBM’s new Granite 4.0 model family, for example, can outperform older and much larger frontier models at a fraction of the price.

It’s why IBM and other tech companies have embraced small language models (SLMs) for many enterprise tasks. Running them requires less computational power, memory, and electricity, and as a team at Stanford recently found, they can capably handle most AI tasks from a laptop or phone. To mark this milestone, the team proposed rating LLMs by their size, through a so-called “intelligence per watt” ratio.

IBM Granite models already stand out by this measure. But what if their intelligence could be augmented further simply by building applications a bit differently, in a more SLM-friendly way? IBM recently released Mellea, a new open-source library designed to make interacting with a language model as predictable as any other software by,…