Rocks, Pebbles and Sand: Modality-aware Scheduling for Multimodal Large Language Model Inference

1. 1Identify the different modalities (e.g., text, images, videos) and their corresponding resource requirements
2. 2Develop a scheduling system that prioritizes tasks based on their modality and resource demands
3. 3Implement a hierarchical scheduling approach, such as Rocks, Pebbles, and Sand, to allocate resources efficiently
4. 4Evaluate the performance of the scheduling system using metrics such as latency, throughput, and memory usage

AI engineers and researchers working on multimodal LLMs can benefit from this approach to optimize inference performance and reduce latency, while product managers can utilize this to improve user experience

💡 Prioritizing tasks based on their modality and resource requirements can significantly improve the inference performance of multimodal LLMs

🚀 Modality-aware scheduling for multimodal LLMs reduces latency and improves performance!