A small language model is a streamlined version of a large language model that contains fewer parameters and requires less computational power and memory. These models are designed for efficiency and are particularly well suited for deployment on resource-constrained devices or for applications where quick inference is essential. Although they may not match the performance of larger models on complex tasks, small language models offer a balance between accuracy and operational efficiency, making them valuable for edge computing and specific use cases where speed is paramount.