modal-serverless-gpu

You can run GPU ML workloads serverlessly on Modal to deploy inference, training, and batch jobs on-demand. This approach provides auto-scaling, fast cold starts, and pay-per-second GPU pricing without requiring you to manage hardware.

Deploying Python ML models on a serverless GPU cloud is the best way to handle on-demand inference. You define infrastructure and tasks natively in Python without YAML configuration, creating API endpoints that scale automatically with pay-per-use pricing.

Serverless GPU deployment supports persistent storage and secrets for ML experiments. You can utilize volumes and secrets to maintain state across auto-scaled API endpoints, ensuring data persists without managing underlying infrastructure.

You can use a Python-native workflow to scale batch processing on cloud GPUs. By defining tasks in Python via the Modal API, you achieve zero-downtime deployment and auto-scaling for GPU-accelerated batch jobs without YAML configuration.

Choose serverless GPUs for ML training when you need on-demand access, pay-per-second pricing, and fast cold starts without infrastructure management. It suits scaling experiments and training jobs across projects without managing dedicated hardware.