modal-serverless-gpu

To deploy an ML model as an API on serverless GPUs, you use Python-native infrastructure definitions to create endpoints with automatic scaling to zero. This approach eliminates infrastructure management while providing on-demand GPU access for inference workloads.

You can access on-demand serverless GPUs ranging from T4 to H100 for batch processing workloads. These GPUs support automatic scaling, allowing you to run experiments and batch jobs without managing the underlying infrastructure.

No, you do not need YAML configuration files for serverless GPU deployment because the platform uses Python-native infrastructure definitions. You define your infrastructure directly in Python code, enabling minimal operational overhead for deploying endpoints.

Yes, Modal version 0.64.0 and later supports auto-scaling for inference APIs, including scaling to zero when idle. This allows on-demand GPU workloads to maintain efficiency without requiring continuous infrastructure provisioning.

Yes, you can run zero-downtime updates for bring-your-own-model workflows on serverless GPUs. The platform enables easy model deployment and inference via API, ensuring endpoints remain operational during updates.

The best way to run ML experiments without managing infrastructure is using serverless GPU workloads with auto-scaling. This method provides on-demand access to GPUs like H100s, letting you run batch jobs and experiments with minimal operational overhead.