The recent developments in the field of large language models (LLMs) have primarily focused on optimizing training and inference processes through parallelism, quantization, and memory efficiency techniques. There is a notable trend towards balancing computational resources and memory usage, with innovations in pipeline and vocabulary parallelism aiming to distribute workloads more evenly across devices. Quantization methods, particularly those involving low-bit precision training and inference, are being refined to reduce model size and computational costs while maintaining performance. Additionally, there is a growing interest in edge-cloud collaborative systems, which leverage efficient feature compression to reduce data transmission overhead and improve inference speed. The field is also witnessing advancements in superconducting digital systems, which promise significant performance gains for both training and inference workloads. Notably, the identification and preservation of 'super weights' and 'super activations' are emerging as critical factors in maintaining model accuracy during quantization. Overall, the research direction is towards making LLMs more accessible, efficient, and scalable across diverse hardware platforms.