modal-serverless-gpu

Deploy ML models as serverless GPU APIs by defining Python infrastructure code that exposes low-latency inference endpoints without managing underlying cloud resources. This enables on-demand GPU access with auto-scaling from zero to many instances.

Serverless GPU computing provides on-demand GPU resources for ML workloads without infrastructure management. You need it when running batch processing or low-latency inference across diverse GPU types while requiring auto-scaling and zero-downtime deployments.

Yes, you can run batch processing jobs on serverless GPUs using Python-native infrastructure definitions without writing YAML. This allows rapid experimentation and iteration on ML workloads while auto-scaling resources as needed.

Serverless GPU deployment supports auto-scaling from zero to many instances, enabling zero-downtime deployments. It provides on-demand GPU resources that scale automatically based on inference workload demand without manual infrastructure management.

The best way to run low-latency ML inference across diverse cloud GPU types is using Python-native serverless infrastructure that auto-scales on demand. This approach eliminates infrastructure management while supporting common ML frameworks and toolchains.