modal-serverless-gpu

Serverless GPU workloads provide on-demand compute for deploying ML models as HTTP endpoints, eliminating the need to manually provision or manage server infrastructure. You can host inference APIs and batch processing jobs directly on cloud GPUs.

On-demand batch inference is handled using serverless GPU compute with auto-scaling, allowing you to execute scheduled GPU tasks across cloud GPUs without pre-provisioning hardware. This approach provides sub-second startup times for efficient processing.

Yes, this approach requires Modal and GPU-enabled container images to manage serverless compute environments. You must install Modal, authenticate your environment, and configure your container to execute GPU workloads and endpoint deployments.

Yes, serverless GPU workloads support Python-native workflows alongside GPU-enabled container images for easy environment management. This allows you to deploy and manage cloud GPU resources programmatically without manual server configuration.

Serverless GPU compute supports auto-scaling with sub-second startup times specifically for hosting ML models as APIs. This ensures your inference endpoints scale automatically with demand while maintaining low latency.

Serverless GPUs are not ideal for workloads requiring persistent state or continuous dedicated hardware, as they are designed for on-demand throughput, API deployment, and scheduled batch processing rather than always-on server provisioning.