Render Network

RENDER, previously known as RNDR, is the native cryptocurrency of the Render Network and serves multiple purposes:

• Transaction Payments: RENDER is used to pay for rendering tasks and computational services on the network.
• Node Rewards: Node operators who contribute GPU resources to complete rendering tasks receive RENDER tokens as compensation.
• Staking: RENDER can be staked to secure the network and participate in governance.
• Governance: Token holders can vote on Render Network Proposals (RNPs), which determine upgrades, policies, and other changes to the protocol.
• Ecosystem Development: RENDER tokens support the network’s growth by funding grants and incentivising community-driven initiatives.

The Render Network was developed by the Render Foundation, a not-for-profit organisation responsible for maintaining the protocol and supporting its community. Key team members include:

• Tristan Relly (Head of Operations)
• Andrew Hyde (Head of Communications)
• Trevor Harries-Jones (Foundation’s Board of Directors)
• Sunny Osahn (Social Engagement Lead)
• Nikola Verba, Zack In, and Alex Nullius (Development).

The foundation oversees the governance process, facilitates strategic decisions, and drives the Render Network’s evolution through its Render Network Proposal (RNP) system.

The Render Network is optimised for AI and GPU-intensive tasks, supporting the growing demand for AI-driven content creation and computation. Key features include:

• AI-Assisted Rendering: The network enables the use of AI tools for rendering optimisation, such as denoising and AI-driven asset generation.
• Generative AI Workflows: Artists and developers can integrate generative AI tools to create textures, models, and dynamic content for ultra-high-resolution 3D environments.
• On-Chain Traceability: All work processed on the network includes hashed metadata, allowing AI training and inferencing with transparent provenance and licensing.
• Distributed GPU Training: The network supports distributed AI training tasks, leveraging idle GPUs for cost-effective machine learning model development.