The field of large language model inference is moving towards optimizing performance, efficiency, and scalability. Researchers are exploring innovative approaches to improve the throughput and latency of large language models, including co-scheduling of online and offline tasks, scalable inference infrastructure, and pipelined offloading for consumer devices. Noteworthy papers include Echo, which introduces a collaborative online-offline task serving system, and AIBrix, which presents a cloud-native framework for optimizing large-scale LLM deployment. Other notable papers are PIPO, which proposes a pipelined offloading framework for efficient inference on consumer devices, FlowKV, which introduces a disaggregated inference framework with low-latency KV cache transfer, and a paper on asynchronous KV cache prefetching, which achieves significant throughput enhancements.