modal-serverless-gpu

You can deploy serverless GPU inference APIs without managing infrastructure by using Modal to run ML functions on-demand. This platform supports scale-to-zero, fast container launches, and exposing inference as REST endpoints via FastAPI.

Yes, you can schedule batch jobs and use dynamic batching for ML workloads on serverless GPUs. Modal supports cron-like scheduling for unattended jobs and allows configuring batching and concurrency to maximize inference throughput.

Modal works with FastAPI to serve ML models in production by supporting ASGI web-serving patterns. You can expose your GPU-accelerated ML inference as REST endpoints suitable for production usage with auto-scaling.

To configure container warm timeouts and persistent model caching for serverless ML deployment, you use Modal's performance and cost controls. You can set idle timeouts and attach volumes to ensure efficient model loading and data persistence across executions.

Yes, you need to specify GPU types explicitly when running ML training on serverless platforms. Modal app and function setup requires explicit GPU specs, such as selecting a T4 GPU, alongside container images for production-ready execution.

The best way to run nightly batch inference and on-demand text generation using serverless GPUs is deploying an auto-scaling endpoint on Modal. You can schedule daily cron-like jobs that load models efficiently via persistent volumes.