The latest paper by Dr. Tian Yuan栋's team addresses the memory and input length constraints encountered by large language models during practical deployment, enhancing the throughput of the inference system by nearly 30 times. The paper introduces a novel approach to implementing KV caching, which significantly reduces memory usage by identifying and retaining important tokens, and performs well in tasks with long input sequences. This achievement will be showcased at NeurIPS'23, holding significant implications for the deployment and application of large language models.