Recent progress in large-scale foundation models has demonstrated strong generalization across diverse language tasks, yet their rapidly increasing parameter counts introduce significant computational, energy, and deployment constraints. These limitations motivate the development of specific and small language models (SLMs) that are optimized for domain-restricted tasks and resource-constrained environments. By reducing model scale while maintaining task-specific expressivity, SLMs offer improved latency, memory footprint, and throughput efficiency, enabling deployment on edge infrastructure, private enterprise clusters, and embedded systems. From a systems perspective, SLMs address critical challenges related to energy-efficient inference, limited compute budgets, and memory-bound workloads, which are increasingly relevant for real-time AI services. Furthermore, in sensitive domains such as healthcare, finance, and industrial automation, smaller deployable models facilitate data privacy preservation and technological sovereignty, allowing organizations to operate AI systems locally without transferring sensitive data to centralized model providers. However, achieving competitive performance with reduced model capacity introduces several open research problems. Key directions include parameter-efficient fine-tuning (PEFT) for domain adaptation, structured sparsity and pruning for compute-efficient architectures, and improved training and inference optimization for high-throughput low-latency serving. Additionally, data-efficient learning, modular architectures, and scalable evaluation protocols remain underexplored in the context of specialized models. Advancing these techniques is essential for enabling efficient, deployable, and sovereign AI systems, positioning SLMs as a complementary paradigm to large-scale foundation models.