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modal-serverless-gpu

Automates ML model deployment as auto-scaling APIs using Python and Modal client.

4|Updated May 14, 2026
Search Tags:#serverless#machine learning#cloud#gpu#modal
One-click install
npx skills add https://github.com/liumeixin/hermes-skills --skill modal-serverless-gpu-liumeixin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: modal-serverless-gpu
Source: https://github.com/liumeixin/hermes-skills/tree/main/mlops/cloud/modal
Command: npx skills add https://github.com/liumeixin/hermes-skills --skill modal-serverless-gpu-liumeixin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires modal>=0.64.0, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill solves the challenge of running ML workloads with on-demand GPU access, eliminating the need for infrastructure management and allowing for the deployment of ML models as auto-scaling APIs or running batch jobs with automatic scaling.

Core Features & Use Cases

  • Serverless GPUs: Access a variety of GPUs like T4, L4, A10G, L40S, A100, H100, H200, B200 without managing infrastructure.
  • Python-native: Define infrastructure in Python code for ease of use.
  • Auto-scaling: Scale to zero or up to 100+ GPUs instantly, with sub-second cold starts.
  • Container Caching: Rapid iteration with image layers cached for performance.
  • Web Endpoints: Deploy functions as REST APIs with zero-downtime updates.
  • Use Case: Ideal for prototyping ML applications quickly, running scheduled jobs, and processing batch jobs with pay-per-second GPU pricing.

Quick Start

Install the Modal client with pip install modal and authenticate with modal setup. To deploy a function, run modal deploy my_function.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 machine learning models as APIs with serverless GPU access?▼

You can deploy machine learning models as auto-scaling REST APIs on Modal's serverless GPU platform by defining your infrastructure in Python code and running the deployment command. This eliminates manual infrastructure management while providing pay-per-second GPU pricing.

What types of GPUs are available for running ML workloads on Modal?▼

Modal provides serverless access to a variety of GPUs including T4, L4, A10G, L40S, A100, H100, H200, and B200. This allows you to match GPU performance to your specific machine learning workload requirements without managing physical hardware.

How do I scale Python-based ML applications to handle varying workloads?▼

Python-based ML applications scale automatically on Modal's serverless platform, instantly scaling from zero to over 100 GPUs with sub-second cold starts. This auto-scaling capability handles varying workloads efficiently without manual intervention.

Do I need to install any specific dependencies to use Modal for serverless cloud computing?▼

Yes, you need to install the Modal client using pip and authenticate your environment with the setup command. The platform requires Python libraries and integrates directly with Python-based applications to define infrastructure natively in code.

Can I run scheduled batch jobs with automatic scaling on serverless GPUs?▼

Yes, Modal's serverless platform supports running scheduled jobs and processing batch workloads with automatic scaling. Container image layers are cached for rapid iteration, and you only pay for the exact seconds your GPU jobs are running.

What is the best way to prototype ML applications quickly without managing infrastructure?▼

Using a serverless GPU platform like Modal is ideal for rapid ML prototyping. It allows you to define infrastructure in Python, deploy functions as web endpoints with zero-downtime updates, and access cached container layers for fast iteration.

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