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

# On-chain AI

> On-chain AI refers to the deployment of machine learning models or inference engines directly within the blockchain's execution environment, such as a virtual machine or a specialized smart contract. While training usually occurs off-chain due to computational intensity, on-chain

# On-chain AI

On-chain AI refers to the deployment of machine learning models or inference engines directly within the blockchain's execution environment, such as a virtual machine or a specialized smart contract. While training usually occurs off-chain due to computational intensity, on-chain AI allows for verifiable inference, where the logic of the model is transparent and results can be proven using zero-knowledge proofs or other verification techniques, ensuring that AI-based decisions in dApps are trustless and deterministic.

## Definition

On-chain AI refers to the deployment of machine learning models or inference engines directly within the blockchain's execution environment, such as a virtual machine or a specialized smart contract. While training usually occurs off-chain due to computational intensity, on-chain AI allows for verifiable inference, where the logic of the model is transparent and results can be proven using zero-knowledge proofs or other verification techniques, ensuring that AI-based decisions in dApps are trustless and deterministic.

## Simple explanation

On-chain AI is like putting a tiny, smart computer program directly inside a smart contract. Instead of asking a website for an answer, the blockchain does the thinking itself, making sure no one can cheat or change the result.

## Why it matters

It brings transparency and verifiable logic to AI-powered dApps. Users no longer have to trust a central server's output; they can verify the AI's decision on the blockchain.

## How it works

Developers convert models into formats compatible with WASM or other EVM-compatible runtimes. Because blockchain nodes are limited in compute power, they often use 'ZK-ML' (Zero-Knowledge Machine Learning) to prove that the AI model calculated a specific result correctly without running the entire model on-chain.

## Real-world example

Giza or Modulus Labs, projects focused on bringing verifiable AI inference to smart contracts.

## Advantages

* Trustless and verifiable AI outputs
* Native integration with DeFi logic
* Permanent and immutable decision history

## Limitations

* Strict computational limits on blockchains
* High gas costs for complex inference
* Model size limitations for current chains

## Common misconceptions

* People think all AI runs on-chain, but training is far too heavy for most networks.
* Many believe it replaces off-chain AI, but it is meant to complement it for verification.

## Related knowledge

* [Smart Contracts](/categories/smart-contracts) — term
* [Zero-Knowledge Proofs](/categories/zero-knowledge-proofs) — term

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**Canonical knowledge ID:** `glossary:on-chain-ai`