> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gamerhash.com/llms.txt
> Use this file to discover all available pages before exploring further.

# What is GamerHash?

> Turning idle consumer GPUs into productive AI compute — backed by an 820,000+ registered platform community worldwide.

GamerHash is a global distributed compute platform, founded in 2017. The network is powered by **idle GPUs in gamers' homes** and consumed by AI applications that need inference at scale.

## The short version

The platform has three sides:

<CardGroup cols={3}>
  <Card title="Contributors" icon="microchip">
    People run the GamerHash AI App on gaming PCs they already own. Approved machines take AI jobs from the network and get paid in GUSD.
  </Card>

  <Card title="The network" icon="network-wired">
    A distributed GPU network that routes AI workloads to whichever machines are available.
  </Card>

  <Card title="Consumers" icon="code">
    AI builders consume the same compute through API integrations or direct partnerships.
  </Card>
</CardGroup>

## What the platform actually does

The [**GamerHash AI App**](https://gamerhash.com/en/app-download) is a free Windows desktop app. When you run it, you can:

* **Use private AI tools locally** — chat, image generation, video generation, voice synthesis, all running on your own GPU. No cloud, no logs, no usage caps.
* **Share idle GPU power** — opt in to the earning module, and the app dispatches AI inference jobs to your machine when it's not in use. You get paid in **GUSD** — the platform's internal earning currency.
* **Earn GHXP** — activity points that accrue for every minute the app runs. Holding GHX raises your account level, and a higher level accrues points faster, up to a cap. The points you lock each month decide where you place in that season's GHX ranking; they do not change what a single AI job pays.

For developers, the same compute is available through an OpenAI-compatible REST API plus an official n8n node, so any application or workflow can route AI workloads to the network without building custom infrastructure.

## Why this exists

The demand for AI compute is exploding, while a vast pool of consumer GPUs sits idle most of the day. GamerHash bridges those two: gamers monetize hardware that would otherwise be wasted, and AI builders get compute at a fraction of cloud cost.

The company has run a distributed network of idle consumer GPUs since 2017. In 2023 that network was repointed at AI inference, and today the platform is focused on AI compute and nothing else.

## Mission

Turn every home PC into a productive node in a global AI compute network — with participation as simple as installing one app.

No specialist hardware, no data-center contracts, no business-account onboarding. The bar to entry is a Windows installer and an RTX-class GPU that's already sitting in the room. That covers running AI locally; earning on top of it needs an approved machine — see [hardware requirements](/ai/hardware-requirements).

## Vision

A future where AI isn't gated by hyperscaler GPU clusters, but powered by millions of consumer machines worldwide — with the value flowing back to the people who own them.

Compute as a household utility, not a hyperscaler privilege. Closer in spirit to Folding\@home or BOINC than to today's centralized cloud — but paid in real money, production-ready, and consumed at scale by AI builders shipping real products.

## Where to next

<CardGroup cols={2}>
  <Card title="A brief history" icon="clock-rotate-left" href="/ecosystem/history">
    Founded 2017 → AI App in 2023 → AI compute network in 2026.
  </Card>

  <Card title="Business model" icon="chart-line" href="/ecosystem/business-model">
    Where revenue comes from and how it's split with users.
  </Card>

  <Card title="Community" icon="users" href="/ecosystem/community">
    Who joins and where the conversation lives.
  </Card>

  <Card title="GamerHash AI" icon="brain" href="/ai/overview">
    The flagship app — modules, hardware, and the earning model.
  </Card>
</CardGroup>