The field of large language models is moving towards more efficient training and inference methods. This is driven by the need to reduce computational resources and environmental impact while maintaining or improving performance. Recent developments have focused on hybrid models, caching mechanisms, and optimization techniques to achieve this goal. Notable advancements include the use of State Space Models and Multi-head Latent Attention layers to improve efficiency without sacrificing accuracy. Additionally, novel frameworks and algorithms have been proposed to accelerate training and inference, such as those utilizing programmable optical fabrics and reducing redundancy in hybrid models. These innovations have the potential to significantly impact the field by enabling more widespread adoption of large language models. Noteworthy papers include Zebra-Llama, which achieves Transformer-level accuracy with near-SSM efficiency, and ECHO-LLaMA, which improves training speed and inference throughput through efficient caching. H2 also stands out for its ability to efficiently train large language models on hyper-heterogeneous clusters.