Small language models: Rethinking enterprise AI architecture

"Specialized data and limited capabilities are just fine for some workflows. This realization is driving the evolution of small language models (SLMs), rather than one-size-fits-all LLMs."

"The pattern is closer to a better division of labor. A routing architecture sends simple or well-scoped queries to a specialized small model, and complex queries to a large model."

"SLMs typically fall in the 1 billion to 7 billion parameter range. Generally, anything below 10 billion is considered small."

"Techniques like knowledge distillation, pruning, and quantization help contain model size without compromising performance."

Large language models (LLMs) are powerful but costly and resource-intensive. Specialized small language models (SLMs) are evolving to provide faster, cheaper, and more private solutions. SLMs, which include domain-specific and statistical models, typically have 1 to 7 billion parameters and are trained on high-quality datasets. Techniques like knowledge distillation, pruning, and quantization help maintain performance while reducing size. A routing architecture allows simple queries to be handled by SLMs and complex ones by LLMs, creating a better division of labor in AI workflows.

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