M*: A Modular, Extensible, Serving System for Multimodal Models

Atindra Jha; Naomi Sagan; Keisuke Kamahori; Irmak Sivgin; Rohan Sanda; Steven Gao; Mark Horowitz; Luke Zettlemoyer; Olivia Hsu; Jure Leskovec; Baris Kasikci; Stephanie Wang

arXiv · 2606.12688

M*: A Modular, Extensible, Serving System for Multimodal Models

Atindra Jha·Naomi Sagan·Keisuke Kamahori·Irmak Sivgin·Rohan Sanda·Steven Gao·Mark Horowitz·Luke Zettlemoyer·Olivia Hsu·Jure Leskovec·Baris Kasikci·Stephanie Wang

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Abstract

We are entering a new era of composite model architectures that integrate diverse components such as vision encoders, language backbones, diffusion and flow heads, audio codecs, action generators, and world-model predictors. Such architectures underpin a broad class of multimodal models, including unified multimodal models, omni models, speech-language models, vision-language-action policies, and world models. However, existing model serving frameworks were built on narrow assumptions about model structure, making them ill-suited to accommodate this new architectural diversity. Here we present M*, a universal serving system for efficient serving of composite AI models. M* represents models as dataflow graphs, processing requests spanning diverse modalities and tasks as traversals over these graphs. The core insight is a modular abstraction that supports arbitrary composition of model components, flexible placement onto a physical cluster, and model-agnostic optimizations within a distributed runtime. We call this abstraction the Walk Graph and show how it can concisely capture composite models from a broad range of families. We instantiate M* on representative models and find that it achieves, on average, 20% lower end-to-end latency than vLLM-Omni for text-to-image workloads on BAGEL, while delivering up to 2.9x lower real-time factor and 2.7x higher throughput for text-to-speech workloads on Qwen3-Omni. M* also outperforms the V-JEPA 2-AC rollout baseline for robotic planning by up to 12.5x. Thus, our work paves the road towards more efficient serving of complex models with minimal developer effort.

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Atindra Jha, Naomi Sagan, Keisuke Kamahori, Irmak Sivgin, Rohan Sanda, Steven Gao, Mark Horowitz, Luke Zettlemoyer, Olivia Hsu, Jure Leskovec, Baris Kasikci, Stephanie Wang. 2026-06-13. M*: A Modular, Extensible, Serving System for Multimodal Models. https://arxiv.org/abs/2606.12688

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