Render Token: get paid to help Hollywood
Backed by parent company OTOY, the RNDR team is based out of Los Angeles, with team members throughout the world. The RNDR advisory board boasts industry leaders such as Ari Emanuel (Co-Founder and Co-CEO, WME), JJ Abrams (Chairman and CEO, Bad Robot Productions), and Brendan Eich (Founder and CEO, of Brave Software and BAT), who have all advised RNDR in various capacities in bridging the gap between creating a system that appeals to both cryptocurrency communities and Hollywood studio production pipelines.
Render token is distributed GPU rendering on the blockchain. Render token is a rendering network that connects artists or studios who require GPU computing power with mining partners who would like to rent out their GPU power in return for RNDR tokens, solving a big problem: the size/availability of GPU computation for artists and studios. The aim is to be able to use blockchain protocols to publish and monetize 3D items in the future. According to Render and their CEO (Anurag Goel), RNDR is "the first network to transform the power of GPU compute into a decentralized economy of connected 3D assets".
It means there will be someone on a requesting job that needs rendering power or computing power or a rendering job or a cinematic job or anything which demands computing power he will submit on the render network. The render network will make a smart contract and decide how long it will take and how much the cost will be. There will be tens of thousands of node operators with great GPU power. The GPU power will do the rendering job send it back to the requester and they will have their rendering job done. This is basically a network of decentralized GPUs connecting to each other to make a very powerful network.
RNDR is the first blockchain GPU rendering network & 3D marketplace. The RNDR coin is based on impressive rendering technology, which creates a distributed worldwide network of millions of GPU machines and creators that work together.
The render network connects people looking to perform rendering jobs with people with GPU power that can handle the renders. Owners connect their GPUs to the Render network to receive those jobs. Users receive a small percentage of RNDR for the job.
After registering the GPUs on the Render network, the GPU owners become "node operators" and can earn RNDR tokens.
To summarize: User (node operator) has GPU power > Creator needs rendering work > Creator sends their files to the rendering network, and they are assigned to each other > User gets a small percentage of RNDR. In contrast, the creator's task is accomplished. Of course, Render receives a small portion of the RNDR paid out to maintain this fantastic technology and perform the transaction.
The Render Network Architecture In its current state, the Render Network consists of two primary layers:
1. The off-chain rendering network, comprising Creators, Node Operators, the Render Network, and rendering application layer vendors. Node Operators are GPU nodes that provide power to the network.
2. Blockchain layers that handle payments, via RNDR and escrow contracts. By leveraging the blockchains’ public ledgers, all Creator-Node Operator interactions are publicly verifiable, giving Creators, Node Operators, and the Foundation Team the ability to ensure all transactions are processed correctly, and if not, are easy to track and correct.
Node Operators and Creators interact with each other in a distributed network model that is arbitrated by a core network infrastructure of servers.
OctaneBench is a proprietary benchmarking tool (and the most popular benchmark for GPU rendering used today) that measures GPU rendering speeds, measured in OctaneBench Points per Hour (OBh), which determines the pricing of rendering tasks. OctaneBench is used to standardize and benchmark a GPU’s performance, ensuring that Creators are subject to consistent performance-based pricing when requesting work from Node Operators. The cost for any task is measured in OctaneBench and is determined by a Multi-Tier Pricing (MTP) algorithm.
As Node Operators successfully complete jobs on the network, they build the trust needed to receive more jobs. Node Operator reputation is built by timely and accurately completing jobs. Creators build reputation scores by successfully using the network. As creators build a history of successful jobs - with minimized user error - they are able to access larger amounts of concurrent GPU nodes. The reputation score, therefore, helps the network efficiently assign work and reduce unintentional congestion from failed renders or malicious Sybil Attacks. The reputation scoring system is periodically updated to increase network efficiency as the complexity of work and the network’s service offerings evolve over time.
Allocation of Resources
Resource allocation on the Render Network can be broken down into two categories: Job Allocation and Node Allocation, which function as follows:
Job Allocation is the process by which a Creator’s scenes are allocated to Nodes on the network. Currently, the Render Network’s job allocation system prioritizes a Creator’s reputation score alongside their scene size and concurrent nodes available at that time.
The creator’s reputation score factors into their job allocation priority in two ways:
1. Creators with higher reputation scores can run more concurrent jobs on the network than users with lower scores.
2. A priority modifier is added onto each Job based on the Creator’s current reputation score, making Creator’s jobs that have high reputation scores ‘more appealing’ to nodes than equivalent jobs by lower-reputation score Creators.
Node Allocation is the process by which Nodes are assigned rendering work on individual jobs. When jobs are sent to the Render Network, they are assigned to node operators based on factors such as the selected tier, hardware requirements, time on the network, user reputation, and node reputation score, and OctaneBench score.
Privacy and Security
The Render Network is built to handle tasks for a wide range of users, from individual artists to globally recognized studios. When rendering or working with any creative or confidential material, privacy and security is of extreme importance. In order to protect the privacy of Creators and assets in the network, the system uses a combination of end-to-end encryption, hashing, virtualization and secure centralized storage:
● Every scene is broken into many individual assets, which are hashed and encrypted while uploaded to the network.
● Rendered outputs are encrypted before being sent through the network.
● Any asset stored in memory or on a disc is always encrypted.
● All individual frames are watermarked prior to download, ensuring payment is given before scenes are downloaded.
● Individual asset storage is short-term.
The Render Network and AI
The growth of artificial intelligence (AI) has generated an unprecedented demand for computational resources. AI applications necessitate vast computational power, outstripping the capacity of traditional CPU-based systems. Through the Render Network SDK, developers can leverage the network's decentralized GPUs for AI compute tasks ranging from NeRF (Neural Reflectance Field) and LightField rendering processes to generative AI tasks.
Increasingly, 3D artists are introducing AI-generated content into their creative workflows, combining hand-created digital artwork with generative AI processing. With the integration of AI toolsets like Stable Diffusion on the Render Network, the network supports the increasing convergence between traditional and next-generation creative workflows that leverage AI. For example, artists can use artificial intelligence tools to create assets like generative AI textures that are used to render ultra-high resolution immersive 3D worlds on the network. Large-scale art collections using generative AI to vary outputs can also be distributed across the network’s nodes, enabling creators to frictionlessly create AI art collections at near unlimited scale. The rise of artificial intelligence requires new forms of digital traceability and asset verification. The Render Network’s deep levels of on-chain provenance built into each work’s Render Graph enable licensing 3D models for AI training or royalty-based usage.
The Render Network uses AI technology to accelerate and optimize rendering processes. AI denoising in OctaneRender, the engine used in the Render Network client, has been specifically trained to denoise volumes with further training optimizations possible through distributed computing. Scene AI models surface visibility for maximum speed while denoising and rendering Out-of-Core Geometry and Emissive Objects. These models are trained on perceptual models of Material, Spectra Irradiance, and Scene Data and enable accelerated rendering for more complex scenes when scene data exceeds VRAM capacity. These models are periodically updated and can be further trained using decentralized GPU nodes.
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