modal-serverless-gpu

You can deploy machine learning models to serverless GPUs by defining Python infrastructure with app and function decorators, creating scalable REST API endpoints that automatically provision high-performance hardware like H100s or A100s on demand.

Yes, serverless GPU infrastructure facilitates on-demand compute for both model training and batch inference, automatically scaling resources to zero when workloads finish so you avoid the overhead of server maintenance.

Serverless GPU APIs auto-scale based on incoming request volume, providing sub-second cold starts and ensuring you only pay for compute time used during inference while scaling to zero during idle periods.

Yes, Modal supports Python-native infrastructure definition, allowing you to deploy GPU-intensive workloads and scalable API endpoints directly from Python scripts using app and function decorators without external configuration files.

Using a serverless environment is the best way to run GPU-intensive workloads without server maintenance, providing automatic scaling, on-demand access to high-performance hardware, and eliminating the complexity of managing cloud infrastructure.

Serverless GPUs provide auto-scaling APIs and sub-second cold starts for large language model inference, ensuring you only pay for compute time used during active requests rather than provisioning dedicated servers that remain idle between tasks.