modal-serverless-gpu

You can deploy an ML model as an API using serverless GPUs by defining infrastructure in Python code and exposing functions as REST APIs. This allows real-time predictions on incoming data with zero-downtime updates.

The best way to run GPU-intensive ML batch jobs without infrastructure management is using on-demand serverless GPU instances. This approach automates resource setup and scaling, allowing automatic scaling to zero or over 100 GPUs.

No, you do not need YAML to define infrastructure for on-demand GPU access. The setup is Python-native, letting you define infrastructure directly in Python code for containerized execution.

Yes, you can use Modal for rapid prototyping of ML applications with sub-second cold starts. It provides instant scaling and container caching with rapid image layers to speed up iteration.

Available GPUs for serverless ML deployment include T4, L4, A10G, L40S, A100, H100, H200, and B200. These can be accessed on-demand for running model serving and batch processing workloads.

Serverless auto-scaling for deep learning model serving provides instant scalability without idle resource costs. It automatically scales to zero or over 100 GPUs with sub-second cold starts, optimizing both cost and performance.