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

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Cloud Infrastructure

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

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.

Key Features

01Sub-second cold starts with Rust-based container orchestration

02Support for high-performance hardware including H100, H200, and B200 GPUs

03Integrated persistent volumes and secure secret management

04Python-native infrastructure definition without YAML or Dockerfiles

05Auto-scaling GPU compute from zero to 100+ instances instantly

06384 GitHub stars

Use Cases

01Running large-scale batch inference or distributed model training jobs

02Deploying ML models as production-ready, auto-scaling REST APIs

03Prototyping and testing GPU-accelerated applications with minimal overhead

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zechenzhangagi/ai-research-skills modal

For use in Claude.ai and ChatGPT

Download Skill