modal-serverless-gpu

You can deploy machine learning models for serverless inference by defining Python app configurations with GPU selection, container images, and volumes to run scalable API endpoints without infrastructure management.

Yes, you can run multi-GPU training jobs and batch processing by configuring Python workloads with autoscaling, lifecycle hooks, and scheduled jobs to execute GPU-heavy tasks automatically on demand.

The best way to configure serverless GPU containers is through Python-based app definitions that specify GPU types, container images, secrets, and volumes to support production-ready ML pipelines and inference services.

Serverless GPU deployment supports automated scheduled jobs and data pipelines by utilizing Python definitions with lifecycle hooks, batching, and autoscaling to run nightly processing without manual server intervention.

You can debug serverless GPU applications and manage persistent storage by configuring Python app definitions with attached volumes, secrets, and deployment support to maintain data across scalable workload executions.

You should use serverless GPU infrastructure when you need to ship inference APIs, training jobs, or batch processing quickly without the operational overhead of provisioning, scaling, and maintaining physical servers.