modal-serverless-gpu

You can deploy serverless GPU workloads for ML inference by defining Python functions on Modal, which provisions the infrastructure and scales automatically. This removes the need to manually manage VMs while providing on-demand endpoints.

Yes, you can run scheduled batch jobs on serverless GPUs by using cron-like schedules within Modal. This allows you to execute periodic workloads like embeddings, ETL, or model evaluation and pay only for the seconds the GPU is active.

To reduce cold-start times for ML model inference, you can use Modal volumes for persistent storage and model caching. This prevents repeated downloads, while container lifecycle hooks handle efficient warm-up before serving requests.

Deploying serverless GPU workloads supports scaling down to zero when idle. Modal automatically scales your GPU-backed functions up for traffic bursts and down to zero when no longer in use, optimizing costs.

The best way to switch between local testing and production serving is to use Modal's multiple execution modes. You can iterate locally during development and switch to deploy for production-grade, auto-scaling GPU serving.

You need persistent storage like Modal volumes to run efficient training pipelines on serverless GPUs. Volumes cache models and data, reducing repeated downloads and ensuring fast container startup for your workloads.