modal-serverless-gpu

You can deploy a FastAPI inference endpoint with serverless GPU acceleration by defining apps, functions, and web endpoints directly in Python, allowing automatic scaling without managing infrastructure.

Running scheduled GPU batch jobs without servers is achieved by defining cron-style workflows and on-demand GPU functions in Python, enabling cost-efficient scaling and automatic scale-to-zero behavior.

Yes, Modal allows you to serve machine learning models with dynamic batching and concurrency limits by orchestrating runtime behaviors and exposing inference as REST endpoints or full ASGI apps.

To persist large ML models across serverless GPU function calls, you optionally attach volumes to your Python-defined functions, allowing container images to access stored models without reloading.

Serverless GPU execution supports specifying custom GPU types and memory variants for ML deployment by configuring on-demand GPU acceleration settings directly within your Python function definitions.

You manage secrets and container warm-up for low-latency model predictions by configuring runtime behaviors in Python, improving initial response times and securely passing credentials to your serverless functions.