modal-serverless-gpu

You can deploy ML inference workloads on serverless GPUs by defining Python apps and functions to run on Modal, which provides auto-scaling, broad hardware support, and zero infrastructure management for on-demand model serving.

Yes, you can create REST API endpoints for ML models that scale to zero when idle. This serverless GPU platform supports deploying functions as APIs with zero-downtime updates and cost-aware elastic scaling based on demand.

To run batch processing jobs on a serverless GPU cloud, you need a Modal runtime, a supported GPU, and Python code to define apps, functions, and images for your specific GPU-accelerated workload.

Auto-scaling for GPU-accelerated inference scenarios works by elastically provisioning many GPUs based on live demand and scaling to zero when idle, reducing the overhead of provisioning and managing GPU infrastructure.

The best way to run ML training jobs without managing GPU capacity is using a serverless, on-demand GPU platform that handles provisioning automatically, allowing you to focus on Python code while the infrastructure elastically scales.

Yes, this serverless GPU platform supports zero-downtime updates for web services. You can deploy functions as REST API endpoints and apply updates seamlessly without interrupting active ML inference or batch processing traffic.