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modal-serverless-gpu

Deploy inference APIs and batch jobs on Modal's serverless GPU fleet.

Updated Apr 12, 2026
Search Tags:#auto-scaling#modal#gpu-cloud#serverless-gpu#ml-workloads#python-native
One-click install
npx skills add https://github.com/thisismynewfmail-ui/Monika-agent --skill modal-serverless-gpu-thisismynewfmail-ui
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: modal-serverless-gpu
Source: https://github.com/thisismynewfmail-ui/Monika-agent/tree/main/skills/mlops/cloud/modal
Command: npx skills add https://github.com/thisismynewfmail-ui/Monika-agent --skill modal-serverless-gpu-thisismynewfmail-ui

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Many teams need powerful GPU resources for machine learning but want to avoid the overhead of provisioning, maintaining, and paying for idle hardware.

Core Features & Use Cases

  • Serverless GPUs: Access a variety of GPU models (T4, A10G, A100, H100, etc.) that spin up on demand and scale to zero when not used.
  • Python-native definitions: Define infrastructure, functions, and containers directly in Python without separate YAML files.
  • Auto-scaling and fast cold starts: Instantly launch dozens of containers and handle sub-second startup latency.
  • Container caching and image management: Reuse built layers to accelerate iteration cycles.
  • Web endpoints: Deploy FastAPI or ASGI applications as zero-downtime APIs.
  • Scheduling and batch processing: Run cron-like jobs, periodic tasks, and dynamic batching on GPUs.

Typical use cases include deploying inference APIs, running nightly training batches, processing large data pipelines, and experimenting with new model architectures without managing servers.

Quick Start

Ask the agent to create and run a Modal function that displays the GPU status using nvidia-smi.

Frequently Asked Questions about modal-serverless-gpu

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

FAQPage Schema
How do I run machine learning workloads on serverless GPUs without managing infrastructure?▼

You can run machine learning workloads on serverless GPUs by deploying inference APIs, batch training jobs, and scheduled tasks on an auto-scaling GPU fleet that spins up on demand and scales to zero when idle.

What is the best way to deploy a Python inference API with automatic GPU scaling?▼

Deploying a Python inference API with automatic GPU scaling is best achieved by defining infrastructure, functions, and containers directly in Python to leverage fast cold starts and zero-downtime web endpoints.

Does serverless GPU provisioning support fast cold starts for sub-second startup latency?▼

Yes, serverless GPU provisioning supports fast cold starts with sub-second startup latency by instantly launching dozens of containers and reusing built image layers to accelerate iteration cycles.

Can I run scheduled batch training jobs on remote GPUs using Python-native definitions?▼

Yes, you can run scheduled batch training jobs on remote GPUs using Python-native definitions, enabling cron-like jobs, periodic tasks, and dynamic batching without managing servers or separate YAML files.

What GPU models are available for serverless machine learning workloads?▼

Available GPU models for serverless machine learning workloads include T4, A10G, A100, and H100, which provision automatically on demand and scale to zero when no longer required.

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