modal-serverless-gpu

Deploy a serverless GPU API by defining a Python-native app with Modal, packaging your model in a container image, and exposing it through FastAPI or ASGI patterns for web inference without managing servers.

Yes, you can configure batch processing jobs and scheduled tasks on serverless GPUs using Modal volumes and secrets, enabling production-style ML workflows for processing datasets or running recurring model jobs.

Configuring autoscaling for ML inference endpoints requires Python-native app definitions on Modal, specifying GPU selection, container images, and deployment controls to automatically scale model serving based on traffic.

Modal works with FastAPI by using ASGI deployment patterns to turn ML models into web APIs, removing the friction of standing up GPU-backed infrastructure for reliable model serving.

The best way to run GPU ML workloads without managing servers is using Modal's serverless execution environment, which handles container images, volumes, secrets, and deployment controls for inference and training.

You should avoid serverless GPU execution for model serving if your workflow cannot adapt to Python-native app definitions or lacks compatible container images, volumes, and secrets configuration required by Modal deployments.