modal-serverless-gpu

Deploy a machine learning model as a serverless GPU inference API by configuring Modal functions with required decorators, specifying GPU resources, and exposing the endpoint via FastAPI or ASGI. This approach supports scale-to-zero behavior to control costs while handling on-demand inference workloads.

Yes, you can use FastAPI to expose Modal serverless functions for model inference. The Skill supports API exposure through FastAPI, ASGI, or REST-style web endpoints, allowing you to serve text-generation models or other machine learning workloads directly over the web.

Reduce cold-start latency for serverless GPU inference by configuring container idle timeouts, warm container behavior, and persistent storage volumes for cached model artifacts. You can also tune concurrency and use dynamic batching to optimize performance and maintain reliable response times.

Configure auto-scaling and multi-GPU support for ML deployment by defining a Modal App with function and class decorators. You can specify GPU resources, including variants and fallbacks, to handle multi-GPU training and batch processing jobs while managing scale-to-zero execution.

Yes, Modal serverless GPU execution supports scheduled cron workloads and batch processing jobs. You can configure serverless functions with specific GPU resources, concurrency settings, and retries to automate and run GPU-intensive code on a schedule without managing servers.

Manage persistent storage for model artifacts in a serverless GPU environment by configuring Modal storage volumes. These volumes cache models and maintain lifecycle model loading across container restarts, which helps reduce cold-start latency and improve inference performance.