Render Network (RNDR) was founded in 2016 by Jules Urbach, CEO of OTOY, a cloud rendering company. Render was created to solve a critical challenge in the computer graphics and creative industries: the high cost and inefficiency of centralized GPU rendering. Jules Urbach envisioned a decentralized network where anyone with unused GPU capacity could contribute resources to a global rendering platform.
Today, Render demonstrates the potential of combining blockchain with GPU rendering, evolving into a leading platform not only in graphics, but also in AI and decentralized computing.
How does the Decentralized GPU Provider Network work?
Idle GPU utilization: Individuals and/or organizations with unused GPUs can contribute their computing power to the Render Network. These GPU resources are available to anyone who needs rendering services, creating a distributed and scalable network.
How is Blockchain Integration?
Through Smart Contracts on the blockchain identifier the following can be done:
Job Requests: Users submit rendering tasks.
Resource Allocation: GPU nodes are assigned to tasks.
Payments: RNDR tokens are used to pay GPU providers automatically upon task completion.
How does it work on Blockchain?
The blockchain maintains a secure and transparent record of:
Submissions and completions of tasks.
Payment transactions.
Use of resources.
What is the payment mechanism?
RNDR tokens are used as a medium of exchange for rendering tasks.
To incentivize GPU providers, GPU owners earn RNDR tokens for their contributions.
What are the Benefits of Combining Blockchain with GPU Rendering?
Rendered assets are encrypted, ensuring privacy and protection.
Blockchain ensures that tasks are completed accurately before payments are released.
Cost efficiency: By bypassing traditional centralized render farms, costs are reduced.
Scalability: A decentralized network can scale based on demand.
Transparency: Blockchain ensures trust and accountability in transactions.
Global Access: Anyone with a GPU can contribute and users can access global resources.
What are the real world applications?
3D content creation: films, video games and VR/AR applications.
AI Workloads: Training machine learning models using decentralized GPUs.
Scientific Simulations: High-performance computations for research.