Small Language Models (SLMs) are compact versions of larger language models designed to perform natural language tasks with fewer parameters. They provide essential language understanding and generation capabilities but require less computational power and memory.
SLMs are trained on targeted datasets with optimized architectures that balance performance and size. They use techniques like pruning, quantization, and knowledge distillation to reduce model complexity while retaining core functionalities, enabling faster inference on devices or edge servers.
For AI product managers, SLMs offer lower costs and reduced latency, improving user experience especially on resource-constrained devices. They enable scalable deployment, faster iteration, and support privacy-sensitive applications by allowing on-device processing, driving business value through accessibility and operational efficiency.