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Andrew-Girgis
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huggingface-zerogpu

Optimize Gradio Spaces for Hugging Face ZeroGPU hardware with CUDA and concurrency best practices.

Updated Jun 15, 2026
Search Tags:#concurrency#ML demo#Gradio#GPU optimization#ZeroGPU#Hugging Face Spaces
One-click install
npx skills add https://github.com/Andrew-Girgis/token-holdem --skill huggingface-zerogpu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-zerogpu
Source: https://github.com/Andrew-Girgis/token-holdem/tree/main/.agents/skills/huggingface-zerogpu
Command: npx skills add https://github.com/Andrew-Girgis/token-holdem --skill huggingface-zerogpu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides guidelines and best practices for developing and running Gradio Spaces on Hugging Face Spaces ZeroGPU hardware, optimizing performance and managing resource usage effectively.

Core Features & Use Cases

  • ZeroGPU Best Practices: Offers detailed rules and patterns for ZeroGPU hardware, including CUDA usage, memory bandwidth, and compute constraints.
  • ML Demo Guidelines: Assists in coding ML demos for ZeroGPU hardware, covering @spaces.GPU decorator usage, model instantiation, and GPU-bound function decorations.
  • Concurrency and Error Handling: Provides guidance on handling concurrency, process isolation, and common errors related to ZeroGPU, ensuring smooth execution.
  • Use Case: A developer wants to create a Gradio Space for a text generation model that runs on ZeroGPU. This Skill helps them understand how to instantiate models, manage durations and quotas, and handle potential concurrency issues.

Quick Start

Implement ZeroGPU optimizations by adding @spaces.GPU to your model inference function and configure your duration based on expected workload.

Frequently Asked Questions about huggingface-zerogpu

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

FAQPage Schema
How do I optimize a Gradio Space for Hugging Face ZeroGPU hardware?▼

To optimize a Gradio Space for Hugging Face ZeroGPU hardware, apply the `@spaces.GPU` decorator to your model inference function and configure the duration parameter based on your expected workload.

Why does my ML demo fail during concurrency on Hugging Face ZeroGPU?▼

ML demos fail during concurrency on ZeroGPU due to process isolation and resource constraints. You need to follow specific guidelines for handling concurrency, model instantiation, and error handling to ensure smooth execution.

Do I need to check CUDA availability when running Gradio Spaces on ZeroGPU?▼

Yes, checking CUDA availability is a fundamental best practice for running Gradio Spaces on ZeroGPU. Proper CUDA usage ensures efficient memory bandwidth management and helps navigate compute constraints.

What is the best way to manage durations and quotas for ML demos on ZeroGPU?▼

Managing durations and quotas for ZeroGPU ML demos involves configuring the `@spaces.GPU` decorator duration parameter based on your expected workload and following resource management best practices to avoid hitting hardware limits.

How do I instantiate a text generation model on Hugging Face ZeroGPU?▼

Instantiating a text generation model on ZeroGPU requires following specific model instantiation guidelines, decorating GPU-bound functions correctly, and managing memory bandwidth to handle compute constraints effectively.

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