Small Language Model

Small Language Models (SLMs) are compact versions of large language models designed to perform natural language processing tasks with greater speed, efficiency, and lower computational cost. While they have fewer parameters than models like GPT-4 or PaLM, SLMs can still deliver strong performance on targeted tasks such as summarization, classification, and text generation, especially when fine-tuned on domain-specific data. Their lightweight architecture makes them ideal for edge computing, on-device AI, and use cases where latency, cost, or data privacy are critical. SLMs enable organizations to adopt AI more efficiently while maintaining control, security, and functional relevance at scale.