# Aight: Decentralized Community LLM Inference Network, Turning Local Computing Power into On-Chain Assets

> Aight is an on-chain LLM inference market based on the DePIN concept, allowing users to provide local Ollama inference services by staking ETH while offering developers OpenAI-compatible API access.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-01T20:11:20.000Z
- 最近活动: 2026-05-01T20:22:29.828Z
- 热度: 150.8
- 关键词: DePIN, 去中心化AI, LLM推理, Ollama, 区块链, 算力共享, OpenAI兼容, Base Sepolia
- 页面链接: https://www.zingnex.cn/en/forum/thread/aight-llm
- Canonical: https://www.zingnex.cn/forum/thread/aight-llm
- Markdown 来源: floors_fallback

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## [Introduction] Aight: Decentralized Community LLM Inference Network, Turning Local Computing Power into On-Chain Assets

Aight is an on-chain LLM inference market based on the DePIN concept, with the core idea of "Your Hardware, Their Intelligence, Our Network". It allows users to provide local Ollama inference services by staking ETH, converting idle computing power into on-chain assets; at the same time, it provides developers with OpenAI-compatible APIs to obtain inference capabilities in a decentralized manner.

## Project Background: Resolving the Conflict Between LLM Inference Costs and Idle Computing Power

Currently, large model inference costs in the AI field are high, while many individuals and enterprises have idle GPU computing power. Aight attempts to build a bridge through blockchain technology, enabling computing power providers to convert local Ollama inference services into on-chain assets, and allowing developers to obtain LLM inference capabilities in a decentralized, permissionless manner.

## Technical Architecture and Workflow

The Aight system consists of four core components:
1. Smart Contract Layer: AightRegistry contract based on the Foundry framework (deployed on the Base Sepolia testnet), managing operator registration, staking, fund custody, etc.;
2. Gateway Service Layer: Built with FastAPI + LiteLLM, providing OpenAI-compatible interfaces to support seamless integration for developers;
3. Operator Nodes: Run local Ollama instances, establish encrypted tunnels with the gateway via CLI, and receive and process inference tasks;
4. Frontend Dashboard: Pulse, built with Next.js, for real-time monitoring of network status, node health, and other metrics.

## Economic Model and Incentive Mechanism

Aight uses a dual-token economic model to coordinate the interests of all parties:
- Computing Power Operators: Need to stake ETH as service collateral, earn fees based on the number of tokens processed, and high-quality nodes get more traffic allocation;
- Service Users: Prepay and lock funds in the contract custody account, are charged based on actual usage, and can withdraw unused balances at any time.
This design ensures fund security and incentivizes operators to provide stable and high-quality services.

## Application Scenarios and Value Proposition

Aight is suitable for multiple scenarios:
1. Privacy-sensitive scenarios: Data does not need to leave the local environment;
2. Cost optimization: Community-driven model reduces inference costs for long-tail models;
3. Censorship resistance and decentralization: No single point of control, enhancing system resilience;
4. Computing power monetization: Individual GPU holders can convert idle computing power into income.

## Project Status and Development Prospects

Currently, Aight is in the hackathon-level prototype stage, with core functions implemented and open-sourced. Future development directions include:
- Mainnet deployment and production-level security audits;
- Support for more models and quantization optimization;
- Decentralized governance mechanism;
- Cross-chain interoperability;
- Enterprise-level SLA guarantees.

## Conclusion: A Beneficial Attempt at Decentralizing AI Infrastructure

Aight combines blockchain economic incentives with AI inference services to create a win-win market: computing power providers gain revenue, and users get low-cost, privacy-friendly inference services. Although it is in the early stage, its clear architectural design and pragmatic implementation path are worthy of attention, providing a reference for the DePIN and decentralized AI fields.