Large Language Model (LLM) inference presents significant challenges, primarily due to the autoregressive nature of the Transformer model. Unlike the training phase, which relies heavily on compute power, the inference phase struggles with memory and interconnect issues. With the growing demand for AI applications, especially in datacenters, these challenges have intensified. The authors identify four key research opportunities to tackle these problems. First, they propose High Bandwidth Flash, which could provide ten times the memory capacity while maintaining HBM-like bandwidth. This is crucial as LLMs require substantial memory to operate effectively. Second, they discuss Processing-Near-Memory and 3D memory-logic stacking, both of which aim to enhance memory bandwidth. Third, they emphasize the importance of developing low-latency interconnects to improve communication speeds between components. Finally, while the primary focus is on datacenter AI, the authors also consider how these advancements could benefit mobile devices, indicating a broader applicability of their findings. By addressing these hardware architecture challenges, the paper aims to pave the way for more efficient LLM inference, which is becoming increasingly vital in various AI applications. The proposed solutions could lead to significant improvements in performance and efficiency, impacting both large-scale data centers and smaller devices.