Researchers at the University of Michigan have released Cornserve, an open-source distributed serving system for any-to-any multimodal models that achieves up to 3.81× higher throughput and 5.79× lower tail latency compared to existing approaches. The system addresses the unique challenges of serving emerging models that accept and generate combinations of text, image, video, and audio data.
Any-to-Any Models Require Different Serving Architecture
Any-to-any multimodal models represent a significant departure from traditional single-input, single-output models. These models accept combinations of multimodal data as input and generate combinations of multimodal data as output—for example, processing video and audio to produce text and images, or converting text and images into video and audio.
The challenge lies in how different requests traverse different paths through the model's computation graph, with each component having different scaling characteristics. Traditional monolithic or disaggregated serving approaches prove inefficient for this workload pattern.
Three-Part Architecture Optimizes Performance
Cornserve implements three key components to handle any-to-any models efficiently:
Flexible Task Abstraction: The system expresses any-to-any model computation graphs in a way that enables component disaggregation and independent scaling of different model components.
Record-and-Replay Execution: A distributed runtime dispatches compute using an efficient execution model that tracks data dependencies and forwards tensor data directly from producer to consumer, avoiding unnecessary data movement between components.
Automatic Optimization: A planner analyzes model and workload characteristics to find optimized deployment plans without manual tuning.
Built on Kubernetes with 23,000 Lines of Python
The system is implemented on Kubernetes using approximately 23,000 new lines of Python code. The researchers have made Cornserve available as open-source software, with a project website at cornserve.ai and a demo video on YouTube.
The research was conducted by Jae-Won Chung, Jeff J. Ma, Jisang Ahn, Yizhuo Liang, Akshay Jajoo, Myungjin Lee, and Mosharaf Chowdhury at the University of Michigan.
Performance Improvements Across Diverse Models
Cornserve's performance gains were measured across diverse any-to-any models, demonstrating that the approach generalizes well to different model architectures and workload patterns. The up to 3.81× throughput improvement and 5.79× latency reduction represent significant practical benefits for organizations deploying multimodal AI systems.
Enabling Next-Generation Multimodal Applications
Any-to-any models enable applications beyond simple image captioning or text-to-image generation. They support complex multimodal content generation, cross-modal translation, and multi-step creative workflows. Cornserve's efficient serving infrastructure makes these advanced capabilities more practical for production deployment.
The system is available for both research and production use, providing the infrastructure needed to serve the next generation of multimodal AI models efficiently.
Key Takeaways
- Cornserve achieves up to 3.81× higher throughput and 5.79× lower tail latency compared to baseline approaches for serving any-to-any multimodal models
- The system uses flexible task abstraction to express computation graphs and enable independent scaling of model components with different characteristics
- Record-and-replay execution tracks data dependencies and forwards tensors directly between producers and consumers to minimize data movement
- Built on Kubernetes with approximately 23,000 lines of Python, Cornserve is open-source and available at github.com/cornserve-ai/cornserve
- Any-to-any models accept and generate combinations of text, image, video, and audio, enabling complex multimodal applications beyond simple single-input, single-output tasks