NASA’s new dark energy space telescope can also detect killer asteroids
technologyreview.com·3h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
M*: A Modular, Extensible, Serving System for Multimodal Models
TL;DR
Existing model serving frameworks struggle to handle the complexity of modern multimodal models, which integrate various AI components. M* is a new serving system that represents models as dataflow graphs, allowing for flexible and efficient processing of diverse tasks.
✦ Why It Matters
Engineers can leverage M* to efficiently serve complex multimodal models, improving performance and reducing latency.
Key Takeaways
- Explore M* for serving multimodal AI models.
- Utilize the Walk Graph for flexible model composition.
- Achieve lower latency in text-to-image tasks.
- Improve robotic planning efficiency with M*.
Full Summary
As AI models become increasingly complex, integrating components like vision encoders and language backbones, traditional serving frameworks fall short due to their narrow design assumptions. M* addresses this gap by introducing a modular abstraction called the Walk Graph, which allows for the arbitrary composition of model components and optimizations in a distributed runtime.
This system processes requests as traversals over dataflow graphs, accommodating various modalities and tasks. In experiments, M* demonstrated an average of 20% lower end-to-end latency compared to vLLM-Omni for text-to-image workloads and achieved up to 2.9x lower real-time factors for text-to-speech tasks.
Additionally, it outperformed the V-JEPA 2-AC baseline for robotic planning by up to 12.5x. These results indicate that M* can significantly enhance the efficiency of serving complex AI models with minimal developer effort, paving the way for broader applications.