modal-serverless-gpu

Serverless GPU provides on-demand compute for ML workloads without managing infrastructure. You need it to reduce overhead, enabling rapid ML deployment, batch processing, and experimentation with pay-per-second pricing and auto-scaling.

To deploy ML models as APIs, define a GPU-enabled Python function in a script and deploy it with the Modal SDK. This exposes a scalable API for your serverless environment, handling deployment, monitoring, and auto-scaling.

Yes, serverless GPU deployment supports batch processing and experimentation workflows with GPU acceleration. It integrates auto-scaling and pay-per-second pricing to handle variable compute demands efficiently.

Yes, the Modal SDK is required to run ML workloads on serverless GPU. You need it to define Python functions, configure the GPU-enabled environment, and execute serverless deployment with API exposure.

The best way to run ML deployments without managing infrastructure is using serverless GPU. It applies on-demand compute for ML workloads, reducing overhead while providing auto-scaling and seamless ecosystem integration for monitoring.