modal-serverless-gpu

Deploy machine learning models as APIs on serverless GPUs by defining Python-native infrastructure and executing workloads with automatic scaling. This approach provisions per-function GPU allocation and creates REST endpoints without manual infrastructure management.

Batch inference jobs can run with automatic scaling to zero by utilizing on-demand serverless GPU compute. Python-native infrastructure definitions automatically manage resource allocation, scaling up for processing and down when jobs complete to minimize costs.

Serverless GPU compute is an on-demand resource allocation model for running ML workloads without infrastructure management. You need it for deploying models as APIs, executing batch training jobs, or running experiments requiring scalable, per-function GPU access.

Serverless GPU deployment supports Python-native infrastructure definitions, allowing developers to define Modal applications programmatically. This enables automatic container caching, per-function GPU allocation, and REST endpoint creation directly within Python code.

The best way to run ML experiments requiring on-demand GPUs without infrastructure management is using serverless GPU compute. This provides automatic scaling, per-function GPU allocation, and container caching to handle experimental workloads efficiently.

Use serverless GPU compute instead of dedicated instances to avoid infrastructure management and leverage automatic scaling to zero. Serverless platforms provide on-demand GPU access, per-function resource allocation, and Python-native deployment for inference APIs.