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modal-serverless-gpu

Deploy machine learning models as auto-scaling serverless GPU APIs.

4|Updated Apr 19, 2026
Search Tags:#serverless#machine learning#auto-scaling#GPU#model deployment
One-click install
npx skills add https://github.com/ragnarokhaa/hermes --skill modal-serverless-gpu-ragnarokhaa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: modal-serverless-gpu
Source: https://github.com/ragnarokhaa/hermes/tree/main/hermes-cerul-tech-news-package/hermes-cerul-tech-news-package/hermes-agent/skills/mlops/cloud/modal
Command: npx skills add https://github.com/ragnarokhaa/hermes --skill modal-serverless-gpu-ragnarokhaa

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 solves the problem of running GPU-intensive machine learning workloads without the need for infrastructure management, allowing for efficient deployment and scaling of ML models.

Core Features & Use Cases

  • Serverless GPUs: Access to on-demand GPUs (T4, L4, A10G, L40S, A100, H100, H200, B200) without infrastructure management.
  • Model Deployment: Deploy ML models as auto-scaling APIs for continuous inference.
  • Batch Jobs: Run batch processing jobs with automatic scaling and pay-per-second GPU pricing.
  • Use Case: Quickly deploy a model to analyze medical images, scaling resources based on demand.

Quick Start

Deploy a model using the Modal Serverless GPU platform with the following command:

modal run 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 machine learning models to a serverless GPU platform?▼

To deploy machine learning models to a serverless GPU platform, you can use Modal to automate the deployment process. This allows you to run GPU-intensive workloads without infrastructure management and scale resources on demand.

What is the best way to run auto-scaling APIs for machine learning inference?▼

The best way to run auto-scaling APIs for machine learning inference is deploying your models to a serverless GPU platform. This approach provides automatic scaling capabilities and pay-per-second GPU pricing for high-performance workloads.

Does Modal support specific GPUs like A100 or H100 for batch processing?▼

Yes, Modal supports a wide range of on-demand GPUs including T4, L4, A10G, L40S, A100, H100, H200, and B200. You can use these serverless GPUs for batch processing jobs with automatic scaling.

How do I run GPU-intensive workloads without infrastructure management?▼

You can run GPU-intensive workloads without infrastructure management by deploying machine learning models to a serverless GPU platform. This eliminates infrastructure overhead while providing auto-scaling capabilities for continuous inference.

Can I scale medical image analysis models dynamically based on demand?▼

Yes, you can scale medical image analysis models dynamically by deploying them as auto-scaling APIs on a serverless GPU platform. This allows resources to scale based on actual demand for high-performance machine learning workloads.

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