modal-serverless-gpu

You can deploy machine learning models as serverless GPU APIs by defining compute resources directly in Python code. This approach provides on-demand GPU access and auto-scaling REST endpoints to handle traffic spikes without managing underlying infrastructure.

Yes, you can execute batch processing jobs for training, inference, or data processing with automatic scaling. The platform provisions on-demand GPU resources dynamically, ensuring workloads scale efficiently based on job volume.

Serverless machine learning workloads support on-demand access to various GPU types, including T4, L4, A10G, A100, and H100. This allows you to match specific compute requirements to your model inference or training tasks.

No, you do not need to manage infrastructure to run serverless GPU workloads. The platform simplifies resource management by allowing you to define compute environments in Python code, abstracting away server maintenance and provisioning.

Container caching improves serverless GPU deployment by enabling sub-second cold starts. This allows rapid iteration and near-instantaneous scaling for machine learning APIs, minimizing latency when new compute instances are provisioned.

Using serverless GPUs instead of dedicated servers for ML model deployment eliminates infrastructure management and automatically handles traffic spikes. You define resources in Python code, gaining on-demand access to scalable compute without idle server costs.