modal-serverless-gpu

Deploy machine learning workloads on serverless GPUs by defining Python-native app definitions that handle GPU selection, persistent volumes, and secrets management. This approach removes infrastructure overhead by routing inference APIs, batch jobs, and multi-GPU training runs through on-demand compute scaling.

Yes, you can expose models as web APIs for inference on serverless GPU compute. The deployment workflow supports serving FastAPI or ASGI applications, allowing you to update services with zero-downtime deployments for production traffic.

The best way to schedule nightly batch jobs and data processing on GPUs is using cron-like schedules within serverless deployments. This operational tooling fans out tasks automatically, handling throughput via batching while relying on persistent storage.

Manage credentials and persistent model caches for cloud-native deployment by utilizing built-in operational tooling. The system uses persistent volumes for storage and dedicated secrets management for securely handling credentials required by your machine learning applications.

Yes, this serverless GPU approach supports multi-GPU training runs with automatic scaling. You can fan out distributed training tasks and interactive experimentation without provisioning servers, relying on on-demand compute to handle the workload.

You should not use serverless GPUs for machine learning deployment if your workload requires continuous, non-scaling execution or highly customized infrastructure provisioning. This approach is optimized for on-demand scaling, batch jobs, and API serving rather than static server management.