Crynux Network is a decentralized AI compute network that turns edge GPUs into a shared cloud for modern LLM/VLM inference and fine-tuning tasks. Its vssML consensus protocol keeps the network permissionless and open to large-scale node participation while making malicious behavior detectable and punishable, bringing decentralized execution close to centralized-platform efficiency. On top of this compute layer, Crynux enables model and data assets that can support new AI-native DeFi applications.
The key component of Crynux is a robust consensus protocol that enables the permissionless joining and using of the decentralized network by millions.
The ability to identify and penalize all malicious behaviors ensures the ecosystem's sustainability while facilitating healthy growth in the long term.
The innovative vssML technology significantly enhances network efficiency, rivaling centralized platforms while remaining decentralized and permissionless.
As the foundation layer, Crynux Network is composed of the edge nodes, including home computers and mobile devices, who provide hardware to the network in exchange for tokens.
Applications could run tasks such as GPT text generation and Stable Diffusion image generation using various models hosted on the Crynux Network. The integration could be implemented in one-line of code using Crynux SDK.
Model developers use Crynux Network to train/fine-tune their models, and provide models as a service for applications and other developers, earning from the usage of their models.
Mobile devices could also be AI-enhanced by running larger and faster models beyond their current capabilities.
Building on top of the AI services, an innovative DeFi ecosystem could emerge around "Model Assets" and "Data Assets". All the current DeFi applications could be reimagined using the brand-new assets as their base assets.
For example, the developers of AI models can tokenize the models using Crynux, sharing the rewards from model usage with the model token holders.
Model tokens can be used as collateral in various DeFi applications. These applications can be deployed directly on the Crynux Blockchain or as modular L2 chains that connect to Crynux via cross-chain communication. Existing DeFi applications on other blockchains are also supported.
By utilizing the Blockchain, Zero-knowledge Proofs and Privacy Preserving Computation technologies, Crynux aims to build a completely decentralized and trustless infrastructure that is always accessible to everyone.
Lithium Network is the first mainnet release of Crynux Network. It turns Crynux into a production AI computing network where applications can use decentralized GPU nodes for LLM inference, vision-language model tasks, image generation, and model fine-tuning.
Applications can use Crynux for LLM inference, Vision Language Model tasks, image generation, and model fine-tuning through familiar APIs.
How to run LLM using Crynux Network
Lithium integrates with the AI ecosystem developers already use. Through the OpenAI-compatible Crynux Bridge API, applications can connect Crynux to agent frameworks, tool-use workflows, LangChain, LangGraph, Hermes Agent, and Vision Language Model applications without rebuilding their stack around a new protocol.
Delegated staking lets CNX holders participate in network rewards without running a node. Stake CNX to reliable node operators through Crynux Portal, support the compute providers you trust, and start sharing in the rewards generated by the network.
Read more about Lithium Network
To start a node on your local computer to earn tokens:
Windows:
Mac:
Other systems:
If you are an application developer who want to utilize the AI abilities provided by the Crynux Network, follow this guide to connect your application to the network.
Checkout our latest research papers about Crynux Network on the ResearchGate:
Crynux Hydrogen Network (H-Net): Decentralized AI Serving Network on Blockchain
Join the Crynux community on Discord:
Crynux homepage:
The Crynux Blog contains the technical explanations and our latest progress:
The technical documentation:
And follow us on Twitter: