#
Spaces ZeroGPU: Dynamic GPU Allocation for Spaces

ZeroGPU is a shared infrastructure that optimizes GPU usage for AI models and demos on Hugging Face Spaces. It dynamically allocates and releases NVIDIA H200 GPUs as needed, offering:

1. Free GPU Access: Enables cost-effective GPU usage for Spaces.
2. Multi-GPU Support: Allows Spaces to leverage multiple GPUs concurrently on a single application.

Unlike traditional single-GPU allocations, ZeroGPU’s efficient system lowers barriers for developers, researchers, and organizations to deploy AI models by maximizing resource utilization and power efficiency.

##
Using and hosting ZeroGPU Spaces

- Using existing ZeroGPU Spaces
  - ZeroGPU Spaces are available to use for free to all users. (Visit the curated list).
  - PRO users get x5 more daily usage quota and highest priority in GPU queues when using any ZeroGPU Spaces.
- Hosting your own ZeroGPU Spaces
  - Personal accounts: Subscribe to PRO to access ZeroGPU in the hardware options when creating a new Gradio SDK Space.
  - Organizations: Subscribe to the Enterprise Hub to enable ZeroGPU Spaces for all organization members.

##
Technical Specifications

- GPU Type: Nvidia H200 slice
- Available VRAM: 70GB per workload

##
Compatibility

ZeroGPU Spaces are designed to be compatible with most PyTorch-based GPU Spaces. While compatibility is enhanced for high-level Hugging Face libraries like `transformers` and `diffusers`, users should be aware that:

- Currently, ZeroGPU Spaces are exclusively compatible with the Gradio SDK.
- ZeroGPU Spaces may have limited compatibility compared to standard GPU Spaces.
- Unexpected issues may arise in some scenarios.

###
Supported Versions

- Gradio: 4+

- PyTorch: Almost all versions from 2.1.0 to latest are supported

  See full list
  - 2.1.0
  - 2.1.1
  - 2.1.2
  - 2.2.0
  - 2.2.2
  - 2.4.0
  - 2.5.1
  - 2.6.0
  - 2.7.1
  - 2.8.0

- Python: 3.10.13

##
Getting started with ZeroGPU

To utilize ZeroGPU in your Space, follow these steps:

1. Make sure the ZeroGPU hardware is selected in your Space settings.
2. Import the `spaces` module.
3. Decorate GPU-dependent functions with `@spaces.GPU`.

This decoration process allows the Space to request a GPU when the function is called and release it upon completion.

###
Example Usage

    import spaces
    from diffusers import DiffusionPipeline

    pipe = DiffusionPipeline.from_pretrained(...)
    pipe.to('cuda')

    @spaces.GPU
    def generate(prompt):
        return pipe(prompt).images

    gr.Interface(
        fn=generate,
        inputs=gr.Text(),
        outputs=gr.Gallery(),
    ).launch()

Note: The `@spaces.GPU` decorator is designed to be effect-free in non-ZeroGPU environments, ensuring compatibility across different setups.

##
Duration Management

For functions expected to exceed the default 60-second of GPU runtime, you can specify a custom duration:

    @spaces.GPU(duration=120)
    def generate(prompt):
       return pipe(prompt).images

This sets the maximum function runtime to 120 seconds. Specifying shorter durations for quicker functions will improve queue priority for Space visitors.

###
Dynamic duration

`@spaces.GPU` also supports dynamic durations.

Instead of directly passing a duration, simply pass a callable that takes the same inputs as your decorated function and returns a duration value:

    def get_duration(prompt, steps):
        step_duration = 3.75
        return steps * step_duration

    @spaces.GPU(duration=get_duration)
    def generate(prompt, steps):
       return pipe(prompt, num_inference_steps=steps).images

##
Compilation

ZeroGPU does not support `torch.compile`, but you can use PyTorch ahead-of-time compilation (requires torch `2.8+`)

Check out this blogpost for a complete guide on ahead-of-time compilation on ZeroGPU.

##
Hosting Limitations

- Personal accounts (PRO subscribers): Maximum of 10 ZeroGPU Spaces.
- Organization accounts (Enterprise Hub): Maximum of 50 ZeroGPU Spaces.

By leveraging ZeroGPU, developers can create more efficient and scalable Spaces, maximizing GPU utilization while minimizing costs.

##
Recommendations

If your demo uses a large model, we recommend using optimizations like ahead-of-time compilation and flash-attention 3. You can learn how to leverage these with ZeroGPU in this post. These optimizations will help you to maximize the advantages of ZeroGPU hours and provide a better user experience.

##
Feedback

You can share your feedback on Spaces ZeroGPU directly on the HF Hub: https://huggingface.co/spaces/zero-gpu-explorers/README/discussions

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