Design a GPU-Efficient Video Service

Design a text-to-video generation platform similar to a modern generative video product. Treat the actual model inference on GPUs as a black box: a job enters a GPU worker and eventually produces a video. Focus on the serving platform rather than model internals. The main requirements are: - Users submit prompts and generation parameters, receive a job ID, and later fetch the result. - GPU capacity is fixed or slow to scale, so the system cannot rely on instant autoscaling. - Traffic is bursty. - The system should maximize GPU utilization while still providing a predictable user experience. - The design should cover queueing, scheduling, admission control, prioritization, storage, failure handling, and observability. Explain how you would design the APIs, control plane, worker architecture, scheduling strategy, and overload behavior.