The field of large language models (LLMs) is rapidly evolving, with a focus on improving efficiency and reducing computational demands. Recent developments have centered around quantization techniques, which aim to reduce the precision of model weights and activations without sacrificing performance. These methods have shown significant promise in enabling the deployment of LLMs on resource-constrained devices. Another area of research is the development of novel hardware architectures, such as photonic chips and near-memory processing, which can accelerate LLM inference and training. Additionally, software-hardware co-design approaches have emerged as a key strategy for optimizing LLM performance and efficiency. Noteworthy papers in this area include 'What Is Next for LLMs? Next-Generation AI Computing Hardware Using Photonic Chips', which explores the potential of photonic hardware for accelerating LLMs, and 'LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization', which presents a hardware-software co-designed accelerator for improving the efficiency of protein structure prediction models. Furthermore, papers like 'Ecco: Improving Memory Bandwidth and Capacity for LLMs via Entropy-aware Cache Compression' and 'GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance' have made significant contributions to the development of efficient quantization methods for LLMs. Overall, the field is moving towards the development of more efficient and scalable LLMs, enabled by advances in quantization, hardware, and software-hardware co-design.