The Digital Press

New research argues that small language models (SLMs) are better suited and more cost-effective than large language models (LLMs) for many specialized, repetitive AI tasks. The paper suggests the future of agentic AI lies in deploying SLMs or heterogeneous systems combining multiple models.

Why it matters: SLMs offer sufficient power and greater economy for many AI agents, potentially lowering operational costs significantly.

The big picture: Agentic AI often involves specialized, repetitive tasks where general conversational abilities of LLMs are unnecessary.

The stakes: Shifting from LLMs to SLMs could reshape AI deployment strategies and industry economics, but adoption barriers remain.

Commenters say: Many emphasize the importance of cost efficiency and specialization, while some question whether smaller models can match LLM versatility in complex tasks.