Towards Efficient Large Multimodal Model Serving

• URL: http://arxiv.org/abs/2502.00937v1
• Date: Sun, 02 Feb 2025 22:10:40 GMT
• Title: Towards Efficient Large Multimodal Model Serving
• Authors: Haoran Qiu, Anish Biswas, Zihan Zhao, Jayashree Mohan, Alind Khare, Esha Choukse, Íñigo Goiri, Zeyu Zhang, Haiying Shen, Chetan Bansal, Ramachandran Ramjee, Rodrigo Fonseca,
• Abstract summary: Large multi-modal models (LMMs) are capable of simultaneously processing inputs of various modalities such as text, images, video, and audio. These models pose significant challenges due to their complex architectures and heterogeneous resource requirements. We propose a decoupled serving architecture that enables independent resource allocation and adaptive scaling for each stage.
• Score: 19.388562622309838
• License:
• Abstract: Recent advances in generative AI have led to large multi-modal models (LMMs) capable of simultaneously processing inputs of various modalities such as text, images, video, and audio. While these models demonstrate impressive capabilities, efficiently serving them in production environments poses significant challenges due to their complex architectures and heterogeneous resource requirements. We present the first comprehensive systems analysis of two prominent LMM architectures, decoder-only and cross-attention, on six representative open-source models. We investigate their multi-stage inference pipelines and resource utilization patterns that lead to unique systems design implications. We also present an in-depth analysis of production LMM inference traces, uncovering unique workload characteristics, including variable, heavy-tailed request distributions, diverse modal combinations, and bursty traffic patterns. Our key findings reveal that different LMM inference stages exhibit highly heterogeneous performance characteristics and resource demands, while concurrent requests across modalities lead to significant performance interference. To address these challenges, we propose a decoupled serving architecture that enables independent resource allocation and adaptive scaling for each stage. We further propose optimizations such as stage colocation to maximize throughput and resource utilization while meeting the latency objectives.

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