modal-serverless-gpu

You can deploy ML models as REST API endpoints on serverless GPU infrastructure by containerizing your runtime and configuring on-demand provisioning. This approach enables auto-scaling for high-performance inference without managing dedicated resources.

Serverless GPU deployment provides on-demand GPU resources for ML workloads without infrastructure overhead. You need it for deploying ML APIs, running batch inference, and rapid experimentation in isolated cloud environments.

Yes, you can run batch inference and rapid prototyping in a cloud GPU environment without managing servers. The platform handles auto-scaling and on-demand provisioning for isolated experiments automatically.

Serverless GPU deployment supports auto-scaling and containerized runtimes for inference. It provisions on-demand GPU resources and exposes them through REST API endpoints to handle varying inference loads efficiently.

The best way to run on-demand GPU workloads without dedicated infrastructure is using serverless provisioning with auto-scaling. This method provides containerized runtimes and REST API endpoints for scalable ML inference and experimentation.