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modal-serverless-gpu

Deploy machine learning models as scalable APIs on serverless GPU cloud.

1|1|Updated May 9, 2026
Search Tags:#serverless#machine learning#GPU#model inference#API deployment
One-click install
npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill modal-serverless-gpu-ldzhhxx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: modal-serverless-gpu
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/optional-skills/mlops/modal
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill modal-serverless-gpu-ldzhhxx

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 serverless GPU cloud platform for running ML workloads, allowing users to deploy ML models as APIs without managing infrastructure.

Core Features & Use Cases

  • Serverless GPUs: Access on-demand GPUs for ML workloads without infrastructure management.
  • API Deployment: Deploy ML models as auto-scaling APIs for real-time inference.
  • Batch Jobs: Run batch processing jobs with automatic scaling and GPU acceleration.
  • Use Case: Use this Skill to deploy a model for image recognition as an API, enabling real-time analysis of incoming images.

Quick Start

Deploy your ML model as an API using the 'modal-serverless-gpu' skill.

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 scalable APIs for real-time inference?▼

To deploy machine learning models as scalable APIs for real-time inference, you can use this Skill to run your workloads on serverless GPUs. It handles auto-scaling and GPU acceleration automatically, turning your Python model definitions into accessible API endpoints.

Can I run batch processing jobs with automatic scaling and GPU acceleration?▼

Yes, you can run batch processing jobs with automatic scaling and GPU acceleration. The serverless GPU cloud platform executes batch ML workloads on demand, allocating GPU resources dynamically as needed without requiring manual infrastructure management.

Do I need Python to define and deploy my model for serverless GPU inference?▼

Yes, Python is required to define and deploy your model for serverless GPU inference. The Skill relies on Python scripts for model definition and deployment configuration to properly set up the auto-scaling API endpoints and batch jobs.

What is the best way to access on-demand GPUs for ML workloads without infrastructure management?▼

The best way to access on-demand GPUs for ML workloads without infrastructure management is using a serverless GPU cloud platform. This Skill allows you to deploy models as APIs, handling auto-scaling and GPU resource allocation automatically without manual setup.

Does this serverless GPU platform support auto-scaling for image recognition APIs?▼

Yes, this serverless GPU platform supports auto-scaling for image recognition APIs. You can deploy an image recognition model as an API, enabling real-time analysis of incoming images with automatic scaling and GPU acceleration.

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