modal-serverless-gpu

Serverless GPU deployment for PyTorch models involves wrapping your inference logic in a Python-native application definition and exposing a REST predict endpoint. This approach scales from zero to hundreds of GPUs on demand, mounting persistent volumes for model weights while using secrets for secure credential access.

Serverless GPU compute for distributed ML training eliminates server management by applying Python-native container definitions to transient batch jobs. You can execute multi-GPU training patterns with persistent volumes for artifacts, paying only per second of compute used during the job lifecycle.

Persistent volumes and secrets management are supported for serverless GPU inference workloads. Volumes enable model caching and shared artifact access across scaled instances, while secrets management securely handles credentials required by your ML APIs and training jobs.

Cold-start mitigation for serverless ML inference APIs is handled through container image management and scheduling primitives. By keeping environments warm and optimizing image builds, you reduce latency for production workloads that require dynamic batching and rapid scaling responses.

Scheduled cron-like tasks are supported for serverless GPU workloads on Modal's cloud. You can automate recurring ML training jobs, batch processing, and model updates by leveraging the platform's scheduling primitives alongside persistent volumes for state management.

A broad range of GPU hardware is available for serverless ML workloads, including A100s for intensive training and inference tasks. This hardware access is on-demand, allowing you to select appropriate GPUs for cost-effective scaling without provisioning underlying infrastructure.