This paper discusses the challenges of large language model (LLM) inference and presents compute-in-memory (CIM) as a promising solution. LLMs have transformed natural language processing, but their computational and memory demands are growing exponentially, making inference inefficient and costly. The von Neumann bottleneck, caused by the separation of memory and computation, limits performance and energy efficiency. CIM technologies directly perform computations in memory, reducing data movement and improving efficiency. This paper reviews various CIM architectures and their potential to address the challenges of modern AI systems. It discusses transformer-based models, their computational requirements, and how CIM can accelerate them. The paper also explores hardware acceleration schemes, challenges in CIM design, and the importance of hardware-software co-design. It highlights the benefits of CIM, including reduced latency, energy consumption, and improved scalability. The paper concludes that CIM offers a promising path for efficient LLM inference, but challenges such as analog computation inaccuracies, peripheral overhead, and limited precision must be addressed. The paper also discusses various strategies for CIM acceleration, including algorithmic enhancements, resilience and fault tolerance, hardware-aware training, high-precision techniques, and comprehensive full-circuit design. These strategies aim to improve the efficiency and scalability of LLM inference while addressing the limitations of current hardware and software approaches.This paper discusses the challenges of large language model (LLM) inference and presents compute-in-memory (CIM) as a promising solution. LLMs have transformed natural language processing, but their computational and memory demands are growing exponentially, making inference inefficient and costly. The von Neumann bottleneck, caused by the separation of memory and computation, limits performance and energy efficiency. CIM technologies directly perform computations in memory, reducing data movement and improving efficiency. This paper reviews various CIM architectures and their potential to address the challenges of modern AI systems. It discusses transformer-based models, their computational requirements, and how CIM can accelerate them. The paper also explores hardware acceleration schemes, challenges in CIM design, and the importance of hardware-software co-design. It highlights the benefits of CIM, including reduced latency, energy consumption, and improved scalability. The paper concludes that CIM offers a promising path for efficient LLM inference, but challenges such as analog computation inaccuracies, peripheral overhead, and limited precision must be addressed. The paper also discusses various strategies for CIM acceleration, including algorithmic enhancements, resilience and fault tolerance, hardware-aware training, high-precision techniques, and comprehensive full-circuit design. These strategies aim to improve the efficiency and scalability of LLM inference while addressing the limitations of current hardware and software approaches.