Model compression encompasses techniques that reduce the size and computational requirements of a trained neural network while preserving as much accuracy as possible. Methods include quantization (reducing numerical precision), pruning (removing unnecessary weights), knowledge distillation (training a smaller model to mimic a larger one), and low-rank factorization. Compression makes it feasible to deploy capable AI models on smartphones, edge devices, and cost-effective servers.