modal-serverless-gpu

You can deploy ML inference APIs on serverless GPU infrastructure by defining Python functions and configuring explicit container images with GPU types. This approach allows you to publish functions as REST APIs or ASGI apps using FastAPI without managing servers.

Yes, you can run scheduled batch jobs with GPU support using Modal by defining serverless execution functions. This allows GPU-backed preprocessing and training runs on-demand, scaling to zero with pay-per-use billing when the batch processing completes.

To cache ML models for serverless GPU inference, you configure persistent volumes within your container environment. This keeps model weights readily available across serverless function invocations, preventing repeated downloads and significantly reducing inference latency.

The best way to configure multi-GPU workloads for serverless ML deployment is through Python-first infrastructure definition. You explicitly specify GPU types and configure concurrency or batching settings to optimize your training runs and inference workloads.

Modal supports local execution before deploying serverless GPU functions to the cloud. You can test your Python infrastructure definitions locally, then transition seamlessly to serving endpoints or full cloud deployment for production workloads.

Configuring container images explicitly for serverless GPU execution ensures your specific ML dependencies and system libraries are available. This prevents runtime errors and allows precise environment control for your inference and training workloads.