How does Modal's scaling work?

Modal features automatic scaling that can scale to zero when idle to eliminate costs and burst to hundreds of GPUs instantly to handle high-demand workloads.

Modal is a serverless platform that lets you run code in the cloud, specifically optimized for GPU-heavy machine learning workloads without managing servers or complex infrastructure.

Can I use persistent storage with Modal?

Yes, Modal provides 'Volumes' for persistent storage of large model weights and data, as well as 'Secrets' for secure management of API keys and credentials.

Do I need to write YAML or Dockerfiles to use this skill?

No, Modal uses Python-native definitions, allowing you to specify GPU requirements, container images, and secrets directly within your Python script.

Which GPUs are supported by Modal?

Modal supports a wide range of NVIDIA GPUs including T4, L4, A10G, A100 (40GB and 80GB), H100, H200, and the latest Blackwell (B200) architectures.

Modal Serverless GPU

Name: Modal Serverless GPU
Author: zechenzhangAGI

byzechenzhangAGI

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384

Infraestructura en la Nube

Deploys and manages machine learning workloads on serverless GPU infrastructure using Python-native configurations.

Acerca de

This skill provides a comprehensive guide for developers to leverage Modal's serverless platform for high-performance ML tasks. It enables running GPU-intensive workloads, deploying auto-scaling APIs, and managing batch processing jobs without the overhead of traditional infrastructure management. By using Python instead of YAML for configuration, it streamlines the deployment of models across a wide range of hardware—from T4 to H200 GPUs—while optimizing costs through pay-per-second pricing and scale-to-zero capabilities.

Características Principales

Sub-second cold starts with Rust-based container orchestration
Support for high-performance hardware including H100, H200, and B200 GPUs
Integrated persistent volumes and secure secret management
Python-native infrastructure definition without YAML or Dockerfiles
Auto-scaling GPU compute from zero to 100+ instances instantly
384 GitHub stars

Casos de Uso

Running large-scale batch inference or distributed model training jobs
Deploying ML models as production-ready, auto-scaling REST APIs
Prototyping and testing GPU-accelerated applications with minimal overhead

Acerca de

Características Principales

Sub-second cold starts with Rust-based container orchestration
Support for high-performance hardware including H100, H200, and B200 GPUs
Integrated persistent volumes and secure secret management
Python-native infrastructure definition without YAML or Dockerfiles
Auto-scaling GPU compute from zero to 100+ instances instantly
384 GitHub stars

Casos de Uso

Running large-scale batch inference or distributed model training jobs
Deploying ML models as production-ready, auto-scaling REST APIs
Prototyping and testing GPU-accelerated applications with minimal overhead