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modal-serverless-gpu

Deploy ML workloads on Modal's serverless GPU platform with Python infrastructure definitions.

Updated Mar 2, 2026
Search Tags:#deployment#serverless#api#cloud#gpu#modal#inference
One-click install
npx skills add https://github.com/gigagiova/hermes-agent --skill modal-serverless-gpu-gigagiova
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: modal-serverless-gpu
Source: https://github.com/gigagiova/hermes-agent/tree/main/skills/mlops/modal
Command: npx skills add https://github.com/gigagiova/hermes-agent --skill modal-serverless-gpu-gigagiova

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Running ML workloads on GPU resources without managing infrastructure, on-demand scalability, and API deployment.

Core Features & Use Cases

  • Serverless GPUs with on-demand scaling across GPUs like T4, A100, H100
  • Python-native infrastructure definitions (no YAML)
  • Deploy endpoints and batch jobs, and schedule tasks for automation

Quick Start

Deploy a GPU-enabled Modal app with serverless compute and auto-scaling.

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 ML inference APIs on serverless GPUs without managing infrastructure?▼

Deploy ML inference APIs on serverless GPUs by defining Python-native infrastructure without YAML, enabling auto-scaling and REST endpoints without managing underlying compute resources.

What is serverless GPU auto-scaling for batch processing workloads?▼

Serverless GPU auto-scaling dynamically provisions compute resources like T4, A100, or H100 GPUs for batch processing workloads, scaling to zero when idle to optimize infrastructure costs.

Can I use Python to define infrastructure for Modal GPU deployment instead of YAML?▼

Yes, Modal GPU deployment uses Python-native infrastructure definitions entirely without YAML, allowing you to write code for REST endpoints, scheduling, and persistent resources directly.

Does Modal serverless GPU platform support on-demand A100 and H100 compute?▼

Yes, Modal serverless GPU platform supports on-demand scaling across various GPU types including T4, A100, and H100 for inference services, batch jobs, and automated experiments.

What is the best way to run scheduled batch jobs on serverless GPUs?▼

The best way to run scheduled batch jobs on serverless GPUs is using Modal's Python-based infrastructure definitions, which enable task scheduling, persistent resources, and automated API endpoints.

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