Large language models (LLMs) have traditionally relied on the Transformer architecture, which, due to its quadratic complexity in the self-attention mechanism, becomes computationally expensive and memory-intensive as context length increases. This limitation has led to the development of new architectures, such as the Mamba, introduced by Albert Gu and Tri Dao in December 2023, which employs a selective state-space model (SSM) to achieve linear-time inference and improved throughput without relying on attention mechanisms. This innovation has inspired a wave of hybrid models, combining elements of Mamba with Transformers to optimize efficiency and scalability. Notable developments include Jamba, a large-scale hybrid model by AI21 Labs that interleaves attention and Mamba layers and supports extensive context lengths, and MambaVision, which adapts Mamba for computer vision by integrating it with Transformers for hierarchical processing. These hybrid models, such as Falcon Mamba, Nemotron-H, Bamba, Hunyuan-TurboS, and Phi-4-mini-flash-reasoning, showcase improved computational efficiency, memory management, and scalability across various applications, signaling a shift toward state-space and hybrid architectures as potential new standards in AI model design.