While the AI race is focused on scale, the next breakthrough advantage isn't in bigger models—it's in smaller, smarter ones. Compact models, or small language models (SLMs), are unlocking a new dimension of scalability — not through sheer computational power, but through accessibility. They demand local inference and on-device performance, which compact models deliver — enabling real-time responsiveness, preserving data privacy and simplifying infrastructure. The hidden advantage: Speed to marketWhen compact models come into play, development doesn't just accelerate — it transforms. Because deploying compact models locally — on CPUs or edge devices — removes the weight of external dependencies.