# Intro to Subnet 2

Subnet 2 is a decentralized zero-knowledge machine learning (zkML) proving network built on Bittensor. It provides verifiable AI inference through cryptographic proofs, enabling any computation to be proven and verified trustlessly.

## Network Performance

| Metric                 | Value  |
| ---------------------- | ------ |
| Proofs Generated       | 300M+  |
| Unique Miners          | 1,500+ |
| Proof Time Improvement | 10x    |

Subnet 2's incentive mechanism rewards faster, smaller proofs—driving continuous optimization without central coordination. In 2025, the network improved average proof times by an order of magnitude through competition alone.

## What is Proof-of-Inference?

Proof-of-Inference is the core mechanism that powers Subnet 2. When an AI model runs an inference, the network generates a zero-knowledge proof that mathematically guarantees:

* The specified model was actually executed
* The input data was processed correctly
* The output is authentic and untampered

This transforms AI from a black box into a verifiable system where outputs can be cryptographically proven to be correct.

## How It Works

```
REQUEST → SLICE → PROVE → RESPOND
```

1. **Request**: Clients submit inference requests to the network
2. **Slice**: Computation is distributed across miners
3. **Prove**: Miners generate zero-knowledge proofs in parallel
4. **Respond**: Proofs are aggregated and verified, output is returned as cryptographically verified

## Network Architecture

Subnet 2 operates on Bittensor's incentive network with two key participants:

**Miners** receive input data from validators, run inferences through zk-circuits, and return outputs with accompanying proofs. Miners compete on proof generation speed, proof size, and output accuracy. The network is CPU-intensive, providing opportunities for non-GPU miners.

**Validators** distribute inference requests to miners, verify the returned proofs, and score miners based on performance metrics. Validators can accept external queries, routing organic traffic through the network.

## Applications

### Studio

Build and deploy your own zero-knowledge circuits. Convert AI models into verifiable circuits that can be proven on the Subnet 2 network.

### Stats

Real-time analytics dashboard showing network performance, miner leaderboards, proof generation metrics, and model statistics.

## Links

* [GitHub](https://github.com/inference-labs-inc/subnet-2)
* [Stats Dashboard](https://sn2-stats.inferencelabs.com/)
* [Studio](https://sn2-studio.inferencelabs.com/)
* [Twitter](https://x.com/inference_labs)


---

# 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://sn2-docs.inferencelabs.com/readme.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.