modal-serverless-gpu

You can deploy machine learning inference as an API on serverless GPUs by defining your infrastructure as Python code, allowing the workload to auto-scale based on traffic and scale to zero when idle.

Yes, you can run batch processing jobs on A100 or H100 GPUs without managing servers by executing intensive ML tasks in a serverless environment that handles containerization and auto-scaling automatically.

The best way to scale large language model inference endpoints in the cloud is using infrastructure-as-code to deploy Python functions as REST endpoints that automatically scale to zero when idle.

No, you do not need to manage container images manually for serverless GPU deployment because the serverless environment handles container image management and persistent storage volumes for you.

Serverless GPU compute works for scheduled machine learning training jobs by enabling developers to define infrastructure as Python code for auto-scaling APIs and scheduled jobs on-demand.

You use serverless GPU compute instead of provisioning dedicated cloud instances for ML workloads to avoid the complexity of managing GPU infrastructure while gaining auto-scaling, containerization, and on-demand access to T4, A100, or H100 GPUs.