> For the complete documentation index, see [llms.txt](https://docs.w.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.w.ai/compute-rentals/introduction.md).

# Introduction

**Introduction to w\.ai Compute Rentals**

w\.ai offers an innovative solution for accessing rental Jupyter Notebook and SSH containerized environments, allowing users to leverage powerful AI and machine learning frameworks using w\.ai points. With options like Apple's MLX for Python and NVIDIA's Torch with CUDA for transformers and other advanced computations, these environments provide flexible compute capabilities. Workers on the network can seamlessly make their resources available for rent around the globe, earning w\.ai points upon successful rental completions. This approach enables distributed compute resources to drive AI research and development, optimizing both resource utilization and user engagement.<br>

* [Rent Compute](https://app.w.ai/compute)
* [Contribute as a w.ai worker](https://download.w.ai)
* [w.ai Compute Rental Service Terms](https://app.w.ai/compute/terms)
* [Learn about Jupyter Notebooks](https://docs.jupyter.org/en/latest)
* [Learn about SSH](https://docs.w.ai/compute-rentals/ssh)

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