# 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)