modal-serverless-gpu

You can deploy machine learning models to serverless GPUs by wrapping Python functions as REST API endpoints. This approach provides on-demand compute allocation for training and inference, automatically scaling to zero when idle without manual server configuration.

Yes, serverless GPU deployment supports LLM inference endpoints that scale from zero to hundreds of GPUs during traffic spikes. The platform uses pay-per-second pricing and shuts down compute resources completely when no requests are active.

Serverless machine learning workloads support various NVIDIA GPU architectures including T4, A100, and H100. These on-demand GPUs facilitate cost-efficient resource management for training, inference, and batch processing tasks.

To convert a Python script into an auto-scaling API endpoint, use the serverless interface to deploy it as a web endpoint. The platform handles infrastructure-free deployment, automatically allocating GPU compute resources based on incoming request volume.

No, serverless GPU deployment does not require manual server configuration for batch processing tasks. The platform provides infrastructure-free deployment with automatic scaling and on-demand compute allocation across multiple NVIDIA GPU architectures.

Serverless GPUs are not ideal for continuous, always-on machine learning workloads due to the scale-to-zero mechanism. If your inference or training tasks require persistent uptime without cold starts, traditional dedicated GPU infrastructure may be more suitable.