StreamWise: Serving Multi-Modal Generation in Real-Time at Scale
arXiv cs.AI / 3/9/2026
Developer Stack & InfrastructureTools & Practical UsageModels & Research
Key Points
- StreamWise is a modular, adaptive serving system designed to enable real-time multi-modal generative workflows at scale, integrating language, audio, image, and video models.
- The system is demonstrated through real-time podcast video generation combining large language models, text-to-speech, and video-audio generation under strict latency and resource constraints.
- StreamWise dynamically manages quality, model parallelism, and resource-aware scheduling across heterogeneous hardware to optimize trade-offs between latency, cost, and output quality.
- The team benchmarks cost and speed trade-offs, showing that while a low-cost GPU setup takes 1.4 hours to render 10-minute video, StreamWise can achieve sub-second startup delay and real-time streaming for under $45.
- This work addresses complex challenges in serving multi-modal generation workloads in real-time, facilitating applications from automated media synthesis to storytelling.
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