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modal-serverless-gpu

Orchestrate serverless GPU workloads on Modal with auto-scaling.

Updated Apr 3, 2026
Search Tags:#deployment#serverless#gpu#modal#inference#autoscaling
One-click install
npx skills add https://github.com/handsomelong922/my-codex-skills --skill modal-serverless-gpu-handsomelong922
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: modal-serverless-gpu
Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/modal
Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill modal-serverless-gpu-handsomelong922

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

On-demand GPU access and infrastructure management are pain points for ML teams; this solution provides a serverless platform to run GPU-accelerated workloads without managing hardware.

Core Features & Use Cases

  • Serverless GPUs provide on-demand access to GPUs without managing physical infrastructure.
  • Python-native infrastructure definitions let you write code to describe resources and workflows.
  • Auto-scaling supports scale-to-zero and rapid upscaling to accommodate bursts.
  • Fast cold starts with Rust-based orchestration to minimize startup latency.
  • Container image caching speeds up iteration by reusing layers.
  • Web endpoints enable deploying functions as REST APIs with zero-downtime updates.

Quick Start

Install Modal and define a simple App, then deploy a GPU-enabled function to a REST API.

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 for ML inference without managing physical infrastructure?▼

Serverless GPU workloads run on Modal using Python-native infrastructure definitions to execute ML inference on demand. This platform applies auto-scaling and pay-per-second pricing to provide GPU access without hardware management.

How do I deploy a Modal function as a REST API endpoint for GPU-accelerated inference?▼

Deploy a Modal function as a REST API by defining an App and using the Modal Python SDK to annotate GPU-enabled functions. Web endpoints enable deploying these functions with zero-downtime updates for accelerated inference.

What is the best way to scale GPU-accelerated batch jobs to zero to save costs?▼

Scale GPU-accelerated batch jobs to zero using Modal's auto-scaling capabilities for serverless GPUs. This approach supports rapid upscaling to accommodate bursts while applying pay-per-second pricing during active execution.

Do I need the Modal Python SDK to define infrastructure for serverless GPU deployment?▼

Yes, the Modal Python SDK is required to define infrastructure and orchestrate serverless GPU deployment. It uses App and Function annotations within GPU-enabled runtimes to ensure deterministic, scalable execution.

Why does Modal minimize cold start latency for serverless GPU applications?▼

Modal minimizes cold start latency for serverless GPU applications through its Rust-based orchestration. Container image caching further speeds up iteration by reusing layers, reducing startup delays for on-demand workloads.

Can I use serverless GPUs for ML training jobs or are they limited to inference?▼

Serverless GPUs on Modal apply to running both ML training and inference workloads. The platform orchestrates these GPU-accelerated jobs on demand using properly defined App and Function annotations for scalable execution.

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