modal-serverless-gpu

You can deploy ML models as serverless APIs by defining Python-based infrastructure that provisions on-demand GPUs. This approach uses container caching to achieve sub-second startup times and automatic scaling for inference workloads.

Yes, you can run batch jobs on GPU infrastructure without manual management. The serverless environment automatically provisions and scales cloud resources on-demand, allowing you to execute ML workloads without infrastructure overhead.

Yes, deploying serverless GPU workloads requires the Modal client version 0.64.0 or higher. You must use this Python-based client to define the infrastructure and manage the deployment of your ML models.

Auto-scaling for serverless GPU endpoints dynamically provisions cloud resources based on inference demand. It utilizes container caching to maintain sub-second startup times, ensuring your ML workloads scale automatically without manual intervention.

The best way to achieve sub-second startup times is by using a serverless GPU cloud with container caching. This approach maintains warm containers, enabling rapid iteration and immediate availability for ML model inference and API deployments.

Serverless GPU cloud provides on-demand provisioning and auto-scaling, eliminating the need to manage fixed infrastructure. It allows you to deploy ML workloads as APIs or batch jobs efficiently, paying only for active inference time.