modal-serverless-gpu

You can deploy ML models as auto-scaling APIs by defining infrastructure natively in Python code, eliminating the need for manual server management or YAML configuration files. This approach provides on-demand GPU access and automatically scales from zero to hundreds of instances.

Running GPU-intensive ML workloads on a serverless cloud platform allows you to leverage pay-per-second pricing, ensuring you only incur costs during active compute. The infrastructure automatically scales to zero when demand ceases, optimizing your cloud computing budget.

Modal supports sub-second cold starts for serverless GPU API endpoints by utilizing a Rust-based infrastructure and caching container image layers. This ensures rapid container launches and fast iteration cycles for deploying machine learning functions as REST APIs.

Serverless auto-scaling is ideal for batch processing and high-performance computing, allowing you to instantly scale to 100 or more GPUs on demand. It provides access to various hardware options like A100 and H100 for intensive workloads.

Avoiding YAML in favor of a Python-native API allows you to define cloud infrastructure directly within your codebase, streamlining the deployment of machine learning models. This simplifies version control and integrates seamlessly with existing Python development workflows.

You can access a wide variety of serverless GPUs for machine learning workloads, including T4, L4, A10G, L40S, A100, H100, H200, and B200. This hardware diversity supports diverse needs from basic inference to high-performance computing.