modal-serverless-gpu

Serverless GPU inference APIs can be deployed by provisioning on-demand containers using the Modal SDK. You define your model logic in Python, and the platform automatically handles scaling to zero and sub-second cold starts.

Running GPU-accelerated machine learning workloads on demand is best achieved through serverless containers that provide instant access to GPUs like T4, A10G, A100, and H100. You configure your environment in Python using the Modal SDK.

Exposing ML models as REST APIs is supported natively using serverless GPU containers. You can deploy models as web endpoints or FastAPI APIs directly from your Python environment, enabling zero-downtime updates.

Manually provisioning A100 or H100 GPUs is not required for batch ML processing. Serverless GPU containers automatically allocate the necessary hardware on demand, combining multiple inference requests for better GPU efficiency.

Serverless GPU execution supports pay-per-second pricing by automatically scaling to zero when idle. This eliminates the idle costs and operational overhead typically associated with running expensive GPU hardware.

Using Python-native infrastructure for serverless ML deployment allows you to define functions, containers, and volumes directly in Python using the Modal SDK. This removes the need for separate infrastructure configuration files.