modal-serverless-gpu

You can deploy a machine learning model as a serverless GPU REST API using Python-native infrastructure definitions. This approach provides auto-scaling to zero, sub-second cold starts, and exposes your inference workload as a web endpoint without managing servers.

Yes, you can run scheduled batch training and ETL jobs on serverless GPUs without managing infrastructure. This avoids idle costs and long setup times by providing on-demand access to hardware options like T4, A10G, A100, and H100.

The best way to avoid idle GPU costs is using a serverless GPU platform that auto-scales to zero. This ensures you only pay for compute during active inference requests and eliminates the expense of maintaining provisioned infrastructure.

Yes, serverless GPU compute supports persistent volumes for model caches. It also includes secrets management and container image management, allowing you to securely store credentials and maintain consistent environments across deployments.

You can handle multi-GPU training patterns in a serverless environment by defining the infrastructure natively in Python. This allows you to scale production inference and training workflows across multiple GPUs while maintaining observability and cost controls.

Yes, you can integrate FastAPI with serverless GPU deployments through native ASGI support. This allows you to build scalable web endpoints for your machine learning models while leveraging dynamic batching for low-latency workloads.