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modal-serverless-gpu

Deploy ML models as auto-scaling serverless GPU APIs with Python.

6|3|Updated Jan 29, 2026
Search Tags:#API#serverless#auto-scaling#GPU#ML deployment
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill modal-serverless-gpu-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: modal-serverless-gpu
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/modal
Command: npx skills add https://github.com/jonnabio/ace-framework --skill modal-serverless-gpu-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires modal, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a solution for running ML workloads on a serverless GPU cloud platform, eliminating the need for infrastructure management and offering on-demand GPU access.

Core Features & Use Cases

  • Serverless GPUs: Access T4, L4, A10G, L40S, A100, H100, H200, B200 GPUs on-demand.
  • Python-native: Define infrastructure in Python code.
  • Auto-scaling: Scale to zero or up to 100+ GPUs instantly.
  • Sub-second Cold Starts: Rust-based infrastructure for fast container launches.
  • Container Caching: Image layers cached for rapid iteration.
  • Web Endpoints: Deploy functions as REST APIs with zero-downtime updates.
  • Use Case: Deploy a model for image recognition as an API that automatically scales based on demand.

Quick Start

Install the Modal client and run the following command to deploy your model:

modal deploy my-model.py

Frequently Asked Questions about modal-serverless-gpu

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy an ML model as a serverless API with auto-scaling GPUs?▼

Serverless GPU deployment automates running ML workloads as REST APIs by provisioning on-demand infrastructure. You define resources in Python code and deploy via the Modal client to achieve auto-scaling and sub-second cold starts.

Can I access A100 or H100 GPUs on-demand for machine learning workloads?▼

Yes, you can access on-demand GPUs including T4, L4, A10G, L40S, A100, H100, H200, and B200. The platform provisions these resources instantly to run your ML workloads without requiring infrastructure management.

Do I need a Python environment to run serverless GPU deployments?▼

Yes, you need a Python environment and the Modal client installed. The infrastructure is defined natively in Python code, allowing you to deploy functions as web endpoints with zero-downtime updates.

How does auto-scaling work for serverless ML model APIs?▼

Auto-scaling for serverless ML APIs works by instantly scaling from zero up to 100 or more GPUs based on demand. Rust-based infrastructure enables sub-second cold starts for rapid container launches.

What is the best way to avoid cold start latency when deploying ML models?▼

To avoid cold start latency when deploying ML models, use a serverless platform with Rust-based infrastructure and container caching. This enables sub-second cold starts and caches image layers for rapid iteration.

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