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modal-serverless-gpu

Execute GPU-accelerated ML workloads on Modal with Python SDK.

1|Updated Jun 25, 2026
Search Tags:#auto-scaling#batch processing#modal#inference#gpu cloud#ml deployment#serverless gpu
One-click install
npx skills add https://github.com/Signmanal/VIGIL --skill modal-serverless-gpu-signmanal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: modal-serverless-gpu
Source: https://github.com/Signmanal/VIGIL/tree/main/optional-skills/mlops/modal
Command: npx skills add https://github.com/Signmanal/VIGIL --skill modal-serverless-gpu-signmanal

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the overhead of provisioning, managing, and paying for idle GPU infrastructure for machine learning workloads, letting teams focus on model development instead of cloud operations.

Core Features & Use Cases

  • Serverless GPU Access: On-demand access to T4, A100, H100, and other GPU types with pay-per-second pricing, no idle costs.
  • ML Workload Support: Run model training, large-scale inference, batch data processing, and deploy models as auto-scaling REST APIs.
  • Use Case: A data science team can fine-tune a large language model on 8x H100 GPUs for 2 hours, then deploy the trained model as a public API that automatically scales to zero when not in use, only paying for the compute time consumed.

Quick Start

Use the modal-serverless-gpu skill to deploy your pre-trained text classification model as a scalable API with on-demand GPU access.

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 serverless GPU workloads without managing infrastructure?▼

Serverless GPU workloads run on-demand without manual provisioning by using the Modal Python SDK to define container images and provision compute resources, paying only per second of active execution time.

Can I deploy ML models as auto-scaling REST APIs with on-demand GPU access?▼

ML models are deployable as auto-scaling REST APIs with on-demand GPU access, automatically scaling to zero when not in use to eliminate idle compute costs.

What types of machine learning workloads are supported on serverless GPU infrastructure?▼

Serverless GPU infrastructure supports ML model training, large-scale inference, and batch data processing across various GPU types including T4, A100, and H100.

Do I need to provision GPU cloud instances manually for batch data processing?▼

Batch data processing requires no manual GPU cloud provisioning, as the Modal Python SDK handles container image definition and compute resource allocation automatically upon valid user authentication.

What is the best way to avoid idle GPU costs for machine learning deployment?▼

Avoiding idle GPU costs for ML deployment is achieved through pay-per-second pricing on serverless infrastructure that scales automatically, charging only for actual compute time consumed during workload execution.

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