modal-serverless-gpu

Serverless GPU inference is deployed by creating a Modal function that loads a pretrained model from a shared volume and exposes a POST endpoint for batched predictions. This supports selectable GPU types including T4, L4, A100, and H100.

The best way to run GPU-intensive ML training without managing clusters is using on-demand serverless GPU compute. This provides pay-per-second billing and autoscaling for rapid prototyping of ML workloads without long-lived instances.

Yes, the serverless GPU platform supports persistent storage through volume mounts for model caches. It also includes secret management for private tokens, which enables reproducible deployments and scheduled batch jobs.

Yes, you can expose FastAPI and ASGI endpoints directly through the serverless GPU compute platform. This allows you to deploy REST functions and production inference APIs integrated with your ML workloads.

Autoscaling for serverless ML inference APIs works by automatically scaling resources to handle peak traffic while keeping costs minimal during idle periods. It supports dynamic batching to optimize throughput for deployed models.

You should not use on-demand serverless GPUs if your ML workloads require continuous, long-lived instances rather than batch processing, scheduled jobs, or rapid prototyping. This platform is optimized for pay-per-second billing rather than persistent cluster management.