modal-serverless-gpu

You can deploy machine learning models to a serverless GPU cloud by using Python-native infrastructure definitions to create auto-scaling inference APIs and batch processing jobs that scale to zero when idle.

Running GPU-intensive workloads without managing servers is achieved through serverless cloud infrastructure that handles containerization and scaling automatically, providing pay-per-second GPU billing for T4, A100, or H100 hardware.

Yes, you can create auto-scaling REST or ASGI inference APIs that automatically scale up during high traffic and scale to zero when no requests are processed to minimize compute costs.

Serverless GPU deployment supports sub-second cold starts, allowing Python-native infrastructure to execute heavy ML workloads on demand without noticeable initialization delays.

You can set up batch processing jobs and scheduled tasks by defining the logic in a Python script and deploying it to the serverless cloud infrastructure for automated execution.

You should not use serverless GPU infrastructure for ML deployment if your workloads require continuous execution, as the environment is optimized for on-demand execution and scales to zero when idle to minimize costs.