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KarlinskyS
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modal-serverless-gpu

Run ML workloads on serverless GPUs with auto-scaling and persistent volumes.

Updated Apr 10, 2026
Search Tags:#deployment#serverless#gpu#modal#autoscaling#ml#volumes
One-click install
npx skills add https://github.com/KarlinskyS/hermesSkills --skill modal-serverless-gpu-karlinskys
Or copy as Structured Prompt for Agentâ–¼
Please help me install this Agent Skill.
Skill: modal-serverless-gpu
Source: https://github.com/KarlinskyS/hermesSkills/tree/main/mlops/cloud/modal
Command: npx skills add https://github.com/KarlinskyS/hermesSkills --skill modal-serverless-gpu-karlinskys

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Enables teams to run GPU-intensive machine learning workloads without managing or maintaining GPU infrastructure, eliminating provisioning, scaling, and idle-cost concerns.

Core Features & Use Cases

  • Serverless GPU access: On-demand access to T4, L4, A10G, L40S, A100, H100, H200, and B200 GPUs for training and inference.
  • Python-native deployment: Define functions, container images, and lifecycle hooks in Python for fast iteration and reproducible deployments.
  • Auto-scaling and cost control: Scale to zero when idle, auto-scale to many GPUs for peak loads, and use container caching to reduce build times.
  • Persistent volumes & web endpoints: Mount volumes for model caching and expose functions as REST/ASGI endpoints for production services.
  • Use case: Deploy an auto-scaling text-generation API that loads a large model into GPU-backed containers, serves inference traffic with dynamic batching, and persists model weights on a shared volume.

Quick Start

Install modal, run modal setup to authenticate, then deploy or run your GPU function with modal run script.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 serverless GPUs for ML workloads without managing infrastructure?â–¼

Serverless GPU access for ML workloads is provided through Python-native functions that auto-scale to many GPUs for peak loads and scale to zero when idle, eliminating provisioning concerns.

What serverless GPU types are available for machine learning training and inference?â–¼

Available serverless GPU types include T4, L4, A10G, L40S, A100, H100, H200, and B200 for ML training and inference, accessible on-demand without infrastructure management.

Can I mount persistent volumes for model caching in serverless GPU containers?â–¼

Yes, you can mount persistent volumes in serverless GPU containers for model caching, enabling shared model weight storage across auto-scaling instances.

How do I expose ML inference functions as REST API endpoints with autoscaling?â–¼

Expose ML inference functions as REST or ASGI web endpoints that auto-scale based on traffic, loading large models into GPU-backed containers with dynamic batching.

Does serverless GPU deployment support Python-native container images and lifecycle hooks?â–¼

Serverless GPU deployment supports defining container images, lifecycle hooks, and functions natively in Python, ensuring fast iteration and reproducible deployments with container caching.

When should I not use serverless GPUs for ML workloads?â–¼

Serverless GPUs may not suit ML workloads requiring continuous, uninterrupted GPU access or specialized hardware configurations outside the supported T4 through B200 GPU range.

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