modal-serverless-gpu

To deploy an ML model as an API using serverless GPUs, you define Python-native workflows and expose functions as REST endpoints. Modal handles the infrastructure, providing on-demand GPU access and scalable deployment for APIs.

Yes, you can run batch jobs and experiments with auto-scaling GPUs. The platform scales to zero or to many instances, supporting pipelines, experiments, and scheduled tasks with fast cold starts and container caching.

Serverless GPU configurations for ML workloads support on-demand availability across GPUs like T4, A100, and H100. This provides flexible cost and performance optimization for your specific inference or training requirements.

No, you do not manage infrastructure for serverless GPU deployments. The platform handles on-demand GPU access and auto-scaling, allowing you to run ML workloads without managing infrastructure and paying only per second of usage.

Yes, serverless GPU deployment fully supports Python-based workflows. You can use the Modal SDK (version 0.64.0 or higher) to define and deploy models as APIs and batch tasks using easy Python-native workflows.

The best way to scale ML inference APIs on cloud GPUs is using a serverless platform with auto-scaling capabilities. It scales to zero or many instances with fast cold starts, enabling pay-per-second pricing and flexible cost performance.