modal-serverless-gpu

You can deploy ML models as serverless GPU inference APIs using Modal to host GPU-accelerated endpoints without managing underlying infrastructure. This setup provides auto-scaling and per-request billing for scalable model hosting.

Yes, you can run GPU-accelerated batch processing jobs without provisioning servers using Modal's serverless GPU compute. It manages infrastructure automatically, providing on-demand hardware and auto-scaling for ML batch processing tasks.

Yes, serverless GPU deployment supports auto-scaling and per-request billing. Modal scales compute resources up and down in response to demand, charging only for the actual ML inference or batch processing compute used.

Before hosting ML workloads on serverless GPUs, you need a Modal account and a GPU-capable environment setup. These prerequisites enable deployment of serverless functions and execution of on-demand ML compute without manual infrastructure provisioning.

Choose serverless GPUs for ML deployment when you need quick experimentation, auto-scaling, and per-request billing without infra ops. It is ideal for hosting scalable inference APIs and batch processing jobs where managing dedicated infrastructure is unnecessary.