Small Language Models, typically containing fewer than 3 billion parameters, represent a fundamental shift in AI architecture. Unlike their massive counterparts such as GPT-4 or Claude 3, which require extensive computational resources and cloud infrastructure, SLMs are designed for efficiency and specialized performance. This isn’t just about saving resources – it’s about rethinking how AI can be practically deployed in real-world scenarios.