ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism

The recent publication of 'ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism' marks a significant advancement in the field of artificial intelligence, particularly in the efficient serving of multimodal large language models (MLLMs). These models, which integrate various data types like images, videos, and audio, face challenges due to increased inference overhead and complex processing pipelines. The proposed Elastic Multimodal Parallelism (EMP) addresses these issues by dynamically allocating resources based on request types and inference stages. This innovative approach not only separates requests into independent modality groups but also decouples inference stages, allowing for adaptive scaling and improved resource utilization. As a result, ElasticMM demonstrates remarkable performance improvements, reducing time-to-first-token (TTFT) latency by up to 4.2 times and achieving 3.2 to 4.5 times higher throughput compared to state-of-the-art serving syst…