Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing

This research paper from ArXiv focuses on improving the efficiency of Multi-Stage Large Language Model (MLLM) inference. It explores methods for disaggregating the inference process and optimizing resource utilization within GPUs. The core of the work likely revolves around scheduling and resource sharing techniques to enhance performance.