# Hash Forest

**Hash Forest** is SteamPoint’s elastic GPU cloud designed for large‑scale AI training and inference. By aggregating idle GPUs worldwide and orchestrating them with a distributed, multithreaded execution framework, Hash Forest offers flexible packages—hourly, task‑based, or long‑term rentals. An intelligent scheduler continuously balances cost, latency, and load across multiple data centers and DePIN nodes, automatically allocating the optimal compute resources.

Users need no in‑house clusters: with a single click they gain access to pre‑configured environments that include PyTorch, TensorFlow, PaddlePaddle, and more, as well as advanced features such as notebook debugging, job suspend/resume, and hot image migration. Hash Forest delivers super‑computing‑class performance at a fraction of traditional total cost of ownership (TCO), enabling developers and enterprises to accelerate AI model iteration and commercial deployment.

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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://stempoint.gitbook.io/stempoint/getting-started/quickstart/hash-forest.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.