modal-serverless-gpu

Batch inference on serverless GPUs is executed by defining Python functions that process data chunks, automatically scaling execution across multiple GPUs while utilizing persistent volumes to store and retrieve intermediate model artifacts.

Yes, you can run multi-GPU training without managing infrastructure by provisioning serverless GPU compute directly through Python-native app definitions that handle GPU placement and container images automatically.

The best way to schedule daily ML jobs on cloud GPUs is using Python-native deployment primitives to define cron-style scheduled jobs that execute serverless GPU workloads and refresh model artifacts automatically.

Serverless GPU execution supports persistent storage for model weights by attaching persistent volumes to your Python-native app definitions, ensuring artifacts remain accessible across auto-scaling deployments and batch jobs.

You do not need to manually manage containers to serve inference on serverless GPUs; the Skill handles container images, secrets, and deployment primitives automatically through Python-native app definitions.