NVIDIA researchers estimate that 40% to 70% of an AI agent's LLM calls could be handled by a specialized Small Language Model. This post explains the architecture difference between LLMs and SLMs, compares per-token costs, shows where small models win and where frontier models still lead, and walks through a CMU case study in which a small model beat a frontier model on a budget-approval task at a fraction of the cost, with a five-step quick start guide and FAQ.
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