modal-serverless-gpu

Deploy ML models as auto-scaling REST APIs by using serverless GPU compute to eliminate manual infrastructure management. This approach dynamically scales from zero to hundreds of GPUs based on traffic demand while offering pay-per-second billing.

Yes, you can run batch inference jobs on-demand without provisioning GPU infrastructure by utilizing serverless compute resources. This supports instant provisioning of various GPU types like T4, A100, and H100 for processing large batches of inference jobs.

The best way to scale GPU workloads from zero to hundreds of instances is using serverless cloud compute. It automatically handles dynamic scaling based on demand, ensuring you only pay for active compute seconds while maintaining persistent model storage.

Yes, serverless GPU compute supports scheduled ML workloads and secure credential management for cloud operations. It allows you to configure persistent model storage and securely manage credentials while running auto-scaling deployment jobs.

You no longer need to pay for idle GPU compute resources when deploying machine learning models by using serverless on-demand compute. This architecture satisfies pay-per-second billing requirements, eliminating costs associated with idle capacity and complex cloud configuration.