modal-serverless-gpu

Deploy ML models as APIs using serverless GPUs by defining Python-based infrastructure with the Modal framework, configuring GPU resources, setting up pipelines, and exposing API endpoints for scalable cloud inference.

Serverless GPU inference provides on-demand compute for ML workloads without infrastructure management. You need it for scalable model deployment, batch processing jobs, and accelerating experimentation with per-second pricing.

Yes, you can run batch processing jobs on cloud GPUs without managing infrastructure by writing Python functions with the Modal framework, which handles auto-scaling and flexible GPU configuration for your workloads.

The best way to auto-scale ML inference endpoints is using serverless GPU compute with the Modal framework, which provides per-second pricing, flexible GPU selection, and automatic scaling for production workloads.

No, serverless ML deployment with the Modal framework allows flexible GPU configuration through Python-based infrastructure definitions, letting you specify GPU selection and set up images or pipelines programmatically.