modal-serverless-gpu

Deploying ML models as APIs on serverless GPUs requires Python-based infrastructure definition. You define your model and inference logic in Python, and the environment scales automatically from zero to over 100 GPUs instantly for on-demand processing.

Available serverless GPU types for ML inference include T4, L4, A10G, L40S, A100, H100, H200, and B200. These options allow you to match computational resources to your model's specific throughput and memory requirements.

No, you do not need to write YAML to configure serverless GPU infrastructure. The deployment process is Python-native, meaning you define all infrastructure and scaling parameters directly within your Python code.

Yes, you can use Modal for both real-time inference and batch processing. The platform deploys functions as REST APIs for real-time predictions while also supporting automated batch processing tasks on on-demand GPUs.

Cold starts delay serverless ML deployment startup time. This is handled using a Rust-based infrastructure that enables sub-second container launches, alongside image layer caching for rapid iteration and zero-downtime web endpoint updates.

The best way to scale GPU-intensive ML workloads without infrastructure management is using serverless computing. It provides auto-scaling capabilities that instantly adjust GPU allocation based on real-time inference demand, scaling up to 100+ GPUs or down to zero.