modal-serverless-gpu

You can deploy ML models as APIs without managing GPU infrastructure by using serverless GPU compute. This approach provides on-demand hardware access, auto-scaling, and Python-native infrastructure definitions to expose workloads as REST or gRPC endpoints.

Serverless GPU compute provides on-demand hardware access for training workloads without infrastructure management. You need it when running diverse GPU configurations for inference pipelines or batch jobs and want rapid spin-up and cost-optimized scaling.

Yes, you can use Python to define infrastructure for serverless GPU deployments. This allows you to create Modal Apps with GPU-enabled functions, configure persistent storage, and manage secrets directly in code before deploying as web endpoints.

Yes, serverless GPU compute supports persistent storage and secrets management. These features allow you to securely store models and data, ensuring they remain accessible across auto-scaled deployments and rapid instance spin-ups.

Limitations of serverless GPU resources for ML pipelines include potential cold start delays during rapid spin-up and dependency on Python-based infrastructure definitions. Workloads requiring continuous execution may face cost-optimization challenges compared to persistent instances.