modal-serverless-gpu

You can deploy models as APIs with serverless GPU acceleration by using Modal to assign GPUs per-function, enabling real-time inference and zero-downtime deployment without managing underlying cloud infrastructure.

Running batch inference on cloud GPUs is best handled serverlessly using Modal, which provides on-demand GPU tasks, auto-scaling, and cost-efficient resource usage across cloud providers without infrastructure overhead.

Yes, you can use Modal to scale GPU workloads automatically across cloud environments. It implements auto-scaling and per-function GPU assignment to handle on-demand ML tasks efficiently.

You assign a specific GPU to a function for real-time inference using Modal's per-function GPU assignment constructs, ensuring your serverless workload has the exact hardware required for the task.

Yes, serverless GPU deployment supports zero-downtime updates. Using Modal constructs like App, Function, and Cls, you can implement zero-downtime deployment patterns to update ML models seamlessly.

You should not use a serverless GPU architecture if your ML deployment requires persistent state management or continuous, non-fluctuating compute loads, as serverless infrastructure is optimized for on-demand, auto-scaling batch tasks.