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To deploy a serverless GPU inference endpoint, you package Python functions into cloud containers with optional GPU acceleration and expose them via web endpoints using FastAPI or ASGI patterns. You can run `modal deploy script.py` to serve the API.

Yes, you can run batch processing and parallel workloads by deploying Python functions that auto-scale on demand. This handles high-throughput tasks without managing servers, scaling compute resources dynamically as job volume fluctuates.

You attach persistent storage and credentials to serverless GPU workloads by using Volumes for datasets and model weights, and Secrets to inject credentials securely into the cloud containers running your Python functions.

Available GPUs for serverless training and inference include L40S, A100, and H100. You can select these GPU types on demand to run heavy machine learning workloads without being constrained by local hardware limitations.

Yes, this approach works for scheduled pipelines and model training by converting local scripts into cloud-hosted tasks. It applies deterministic remote execution to run Python code on demand, packaging your logic into containers for reliable scaling.

The best way to scale GPU-accelerated Python tasks is deploying them as serverless functions that autoscale in the cloud. This bypasses local GPU limitations by running code in managed containers with on-demand compute and web endpoints.