modal-serverless-gpu

Serverless GPU compute allows you to execute ML training and inference workloads on-demand without provisioning hardware. You leverage Python-native infrastructure definitions to scale automatically from zero to hundreds of GPUs, paying only per second of compute time used.

Yes, you can run batch inference and training jobs on demand by utilizing serverless GPU compute resources. This setup allows you to execute intensive machine learning workloads dynamically and shut down the compute resources when the jobs are complete.

The best way to access A100 or H100 GPUs without local hardware is using serverless cloud infrastructure. You can spin up powerful GPUs on-demand for fine-tuning large language models and pay only for the exact compute time consumed per second.

Yes, the modal library supports auto-scaling cloud GPU infrastructure from zero to hundreds of GPUs. It allows you to define infrastructure natively in Python and automatically scales compute resources based on real-time workload demands.

Serverless GPU workloads optimize cloud costs through pay-per-second pricing models that charge strictly for compute time used. This prevents paying for idle resources by automatically scaling infrastructure down to zero when training or inference jobs finish.