The Rise of Small Language Models: Why Size Isn't Everything
The Rise of Small Language Models: Why Size Isn’t Everything For years, the narrative was simple: bigger is better. GPT-4 was massive, Claude was massive, and the race seemed to be about who could train the largest model on the most data. But that story is changing. Small language models - typically under 15 billion parameters - are proving that you don’t need 175 billion parameters to solve real problems. The shift isn’t just about efficiency. It’s a fundamental change in how we think about AI deployment, cost, and what actually matters for most use cases. ...