modal-serverless-gpu

You can deploy ML models as scalable APIs by using Python-native workflows to orchestrate serverless GPU execution. This method provides on-demand GPU compute for inference workloads without requiring you to provision or manage underlying cloud infrastructure.

Serverless GPU execution for ML workloads delivers on-demand GPU compute for training and inference tasks without infrastructure management. It enables automatic scaling and Python-based orchestration to run models as APIs or batch processing jobs in the cloud.

Yes, you can run batch inference without provisioning clusters by using on-demand serverless GPUs. This approach handles automatic scaling and resource allocation natively, allowing you to execute batch processing and experimentation without managing underlying infrastructure.

Yes, Modal works with Python for prototyping GPU-accelerated workflows by providing a Python-native cloud environment. It enables you to define serverless GPU execution logic directly in Python, facilitating rapid prototyping and deployment of ML models without infrastructure management.

You should use serverless GPUs instead of managing cloud infrastructure when you need to deploy ML models as APIs or run batch inference with automatic scaling. It removes the burden of provisioning clusters, making it ideal for cloud-based experimentation and ML workloads.