ModServe: Scalable and Resource-Efficient Large Multimodal Model Serving

• URL: http://arxiv.org/abs/2502.00937v2
• Date: Fri, 21 Mar 2025 16:53:47 GMT
• Title: ModServe: Scalable and Resource-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 multimodal models (LMMs) demonstrate impressive capabilities in understanding images, videos, and audio beyond text.<n>We present the first comprehensive systems analysis of two prominent LMM architectures, decoder-only and cross-attention, across six representative open-source models.<n>We propose ModServe, a modular LMM serving system that decouples stages for independent optimization and adaptive scaling.
• Score: 19.388562622309838
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large multimodal models (LMMs) demonstrate impressive capabilities in understanding images, videos, and audio beyond text. However, efficiently serving LMMs in production environments poses significant challenges due to their complex architectures and heterogeneous characteristics across their multi-stage inference pipelines. We present the first comprehensive systems analysis of two prominent LMM architectures, decoder-only and cross-attention, across six representative open-source models, revealing key 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 and bursty traffic patterns. Based on these insights, we propose ModServe, a modular LMM serving system that decouples stages for independent optimization and adaptive scaling. ModServe dynamically reconfigures stages and handles bursty traffic with modality-aware scheduling and autoscaling to meet tail latency SLOs while minimizing costs. ModServe achieves 3.3-5.5x higher throughput (leading to 25-41.3% cost saving) while meeting SLOs on a 128-GPU cluster with production traces.

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