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

# What is XYNQ?

XYNQ is a **community-owned inference network**. It is, in effect, a decentralized cloud for running large language models — except the "cloud" is made of ordinary people's graphics cards rather than a hyperscaler's data center.

There are three roles in the network:

1. **Users** — anyone who opens the terminal and sends prompts. Users pay nothing and create no account.
2. **Contributors** — people who connect a machine and donate idle GPU cycles to serve inference. Contributors earn $XYNQ.
3. **The coordinator layer** — the routing, scheduling, and verification fabric that turns thousands of heterogeneous GPUs into one coherent endpoint.

### The core idea

A single consumer GPU cannot hold a large model in memory, and it cannot serve many users at once. But *thousands* of consumer GPUs, coordinated correctly, can rival a commercial inference cluster.

XYNQ achieves this with three techniques borrowed from large-scale model serving and adapted for an open, untrusted, geographically-distributed fleet:

* **Model sharding** — a model's layers are partitioned across multiple nodes so that no single node needs to hold the whole model.
* **Dynamic scheduling** — requests are routed to the nodes that can serve them fastest, accounting for latency, load, and which shards each node holds.
* **Redundancy and verification** — every result is checked, and work is replicated enough that a node going offline mid-request never breaks your chat.

### What XYNQ is not

* It is **not** a single hosted model behind an API key.
* It is **not** dependent on any one company's data center.
* It does **not** retain your data, profile you, or require identity.

### Design principles

| Principle            | What it means in practice                                                                 |
| -------------------- | ----------------------------------------------------------------------------------------- |
| Openness             | Anyone can use the network and anyone can contribute to it.                               |
| Privacy by default   | The system is architected so that retention is impossible, not merely "off."              |
| Graceful degradation | If capacity drops, latency rises before quality does.                                     |
| Sustainability       | The network funds its own coordination layer through $XYNQ rather than ads or data sales. |


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