Small Models, Serious Intelligence- A shift toward efficient AI

Large Language Models know anything—from the Theory of Relativity to how to build a deck in your backyard. That breadth is genuinely impressive, and it’s why LLMs feel so magical. But that magic comes at a cost, and the very thing that makes LLMs remarkable also makes them inefficient for most real-world tasks.

The core problem with LLMs is inefficiency. When you ask a simple question like “What time is it now?”, the model doesn’t just retrieve the time. It evaluates tens or even hundreds of thousands of tokens and their probabilities, activating vast portions of a network trained on everything from poetry to the Theory of Relativity. Even with Mixture‑of‑Experts (MoE) models, modern LLMs still end up evaluating thousands of tokens and millions of parameters for simple questions. This is wildly disproportionate to the task. It’s like solving advanced physics equations just to check your watch.

Small Language Models (SLMs) take a fundamentally different approach. By being narrower in scope, they require far less compute, consume less energy, and cost significantly less to run. They don’t need to reason about the entire universe to answer a well-defined question. This makes them efficient.

Another key shift is tone and intent. LLMs are built to be general conversationalists, which often results in a neutral, generic voice. SLMs, on the other hand, can be trained to speak like domain experts. A medical SLM can sound like a clinician. A legal SLM can reason like a lawyer. Instead of one model pretending to know everything, we get many models that truly know something.

Training also becomes faster and cheaper. Smaller datasets, shorter training cycles, and reduced infrastructure requirements make SLMs more accessible and easier to iterate on. This lowers the barrier to experimentation and encourages specialization rather than scale for scale’s sake.

The broader trend is clear: AI is moving toward efficiency. Over the next few years, the goal won’t be to make models larger, but to make them smarter per unit of compute. Less waste, more purpose. In that future, small models won’t be a compromise—they’ll be the point.