# Huggingface Zerogpu

AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU.

- Repository: https://github.com/huggingface/skills (11249 stars)
- Category: Writing
- License: Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add huggingface/skills --skill huggingface-zerogpu -g -a claude-code -y`
Codex: `npx skills add huggingface/skills --skill huggingface-zerogpu -g -a codex -y`
Cursor: `npx skills add huggingface/skills --skill huggingface-zerogpu -g -a cursor -y`
Gemini CLI: `npx skills add huggingface/skills --skill huggingface-zerogpu -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add huggingface/skills`

## Description

AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints, pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures, even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Hugging Face ZeroGPU

Rules and patterns for ML demos on Hugging Face Spaces with **ZeroGPU** hardware. Covers `@spaces.GPU`, duration and quota tuning, process isolation, the CUDA availability model, concurrency safety, and CUDA build constraints.

## Scope

This skill is for **Gradio SDK Spaces using ZeroGPU hardware**. Docker and Static Spaces cannot schedule onto ZeroGPU, and Streamlit apps now run as Docker Spaces, so this skill applies only to Gradio. For general Gradio coding (components, layouts, event listeners), see the `huggingface-gradio` skill in this repo. The authoritative ZeroGPU docs live at https://huggingface.co/docs/hub/spaces-zerogpu, refer to them for the current backing GPU, runtime version lists, and tier thresholds, all of which change over time.

## Reference Files

| Reference | When to read |
|-----------|--------------|
| `references/concurrency.md` | Always read alongside SKILL.md when writing ZeroGPU code, handlers run in parallel by default |
| `references/how-zerogpu-works.md` | When reasoning about cold-starts, worker reuse, why module-scope warmup does not carry to requests, or why returning CUDA tensors hangs |
| `references/how-quota-works.md` | When choosing `duration` values, debugging `illegal duration` vs `quota exceeded` errors, or explaining why default 60s blocks short tasks |
| `references/cuda-and-deps.md` | When installing CUDA-dependent packages (e.g. `flash-attn`), pinning torch side-cars, or reading wheel filename tags |

## Hardware

ZeroGPU exposes two GPU sizes that map to a fraction of the backing card:

| `size` | Slice of backing GPU | Quota cost |
|--------|----------------------|------------|
| `large` *(default)* | Half | 1x |
| `xlarge` | Full | 2x |

Default `large` gives half a physical GPU, so memory bandwidth and compute are significantly lower than the full card's specs. Use `xlarge` only when the workload genuinely needs the extra memory or compute.

The rest: https://github.com/huggingface/skills/blob/HEAD/skills/huggingface-zerogpu/SKILL.md