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cloud.google.com·21h 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
- Implement M* to serve complex multimodal models efficiently.
- Utilize the Walk Graph for flexible model composition.
- Expect significant performance gains in latency and throughput.
- Explore M* for applications in text-to-image and text-to-speech tasks.
How It Works
M* uses a modular abstraction called the Walk Graph to represent models as dataflow graphs. This allows for efficient processing of requests across various modalities and tasks by traversing these graphs.
The modular design supports arbitrary combinations of model components and enables optimizations that are agnostic to specific model types, facilitating better resource allocation in distributed environments.