Large Language Models (LLMs) dominate the AI landscape, but a counter-narrative is gaining traction. Researchers are increasingly advocating for Small Language Models (SLMs), highlighting their advantages in efficiency and accessibility. While SLMs may not match the broad capabilities of their larger counterparts, their compact size allows for faster deployment, reduced energy consumption, and operation on resource-constrained devices. This makes them ideal for specialized tasks and edge computing applications, potentially democratizing AI access beyond large data centers.
Small Language Models: Efficiency Over Everything?
