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.

翻译：我们正进入一个复合模型架构的新时代，这些架构集成了多种组件，例如视觉编码器、语言骨干网络、扩散/流头、音频编解码器、动作生成器以及世界模型预测器。此类架构支撑着广泛的多模态模型类别，包括统一多模态模型、全能模型、语音-语言模型、视觉-语言-动作策略及世界模型。然而，现有模型服务框架基于对模型结构的狭隘假设而构建，难以适应这种新兴架构的多样性。本文提出M*——一种用于高效服务复合AI模型的通用服务系统。M*将模型表示为数据流图，将跨模态和任务的请求处理转化为对该图的遍历。核心创新在于一种模块化抽象，支持模型组件的任意组合、在物理集群上的灵活部署以及分布式运行时中的模型无关优化。我们将此抽象称为"行走图"（Walk Graph），并展示其如何简洁地捕获来自广泛模型家族的复合模型。我们在典型模型上实现M*，发现其在BAGEL上的文本到图像任务中，端到端延迟较vLLM-Omni平均降低20%；在Qwen3-Omni上的文本到语音任务中，实时因子降低至2.9倍，吞吐量提升至2.7倍。对于机器人规划任务，M*相比V-JEPA 2-AC滚动基线性能提升高达12.5倍。因此，我们的工作以最小开发代价，为复杂模型的高效服务铺平了道路。