modal-serverless-gpu

You can deploy ML models as APIs without manual infrastructure maintenance by using serverless GPU resources. This approach provides on-demand compute, auto-scaling, and web endpoints to expose ML functions with zero-downtime updates.

Serverless GPU computing for ML workloads provides on-demand compute resources for training, inference, and batch jobs without infrastructure maintenance. It features sub-second cold starts, pay-per-use pricing, and Python-native infrastructure definitions with auto-scaling.

Yes, you can run scalable batch jobs with pay-per-use GPU resources using the serverless platform. It supports container caching and batch processing for cost efficiency, allowing you to handle variable workloads without idle resource costs.

You define cloud infrastructure for ML deployment using Python-native infrastructure definitions. This allows you to specify GPU selection, container images, and auto-scaling rules directly in code before deploying to the serverless platform.

Yes, serverless GPU deployment supports custom container images and auto-scaling. You can configure container caching for cost efficiency and utilize sub-second cold starts to ensure your ML workloads scale dynamically based on demand.

The primary limitation of serverless GPU for rapid ML prototyping involves potential cold start latency, though sub-second cold starts mitigate this. It is optimized for pay-per-use pricing, meaning sustained heavy usage may incur higher expenses than reserved instances.