Inferix Decentralized GPU
  • Getting Started
    • Overview
    • $IFX
    • Resources
    • Brand Kit
    • Frequently asked questions (FAQs)
  • Inferix Whitepaper
    • Introduction
      • Rendering network using crowdsourced GPU
      • Rendering verification problem
    • High-level description of ANGV
      • Noise generation
      • Noise verification
      • Thread model
    • Implementation of ANGV
      • Structure of noise
      • Noise insertion
        • Geometric constraints
        • Distortion region
      • Adaptive noise spreading
      • Verification key generation
      • Noise verification
      • Threat analysis
        • Attacks on verification keys
        • Attacks on noises
        • Attacks on verifiers
      • Performance evaluation
      • Integration
    • Decentralized visual computing
      • Client Apps plugin
      • Client API and SDK
      • Manager node
      • Worker node
      • Decentralized storage
        • Data categories
        • Multi-level 3D polygon data
        • Polygon digester
        • Decentralized storage
        • Decentralized cache
      • Data security with FHE and TEE
        • Verifier data security enhancement with FHE
        • Worker and Manager data security enhancement with FHE
    • Decentralized federated AI
      • Federated learning with TensorOpera
      • Meta LLaMA
      • Stable Diffusion
      • Other AI models
      • Inferix AI
    • Economic model
      • GPU compute market for visual computing and federated AI
      • Inferix vision
      • $IFX token
      • Burn-Mint-Work token issuance model
      • Inferix bench and IBME
        • IB and IBM
        • IBME
      • Price simulation
      • Token metrics and allocation
        • Token allocation
        • Token vesting
      • Governance
      • Node staking and rewards
        • Worker
        • Verifier
        • Manager
        • Penalty pool
      • Node sale and guaranteed node buyback
        • Node sales
        • Guaranteed Node Buyback
    • Future development
      • PoR and NFT minting for graphics creative assets
      • ZKP and PoR communication
      • Inferix RemotePC
      • Rendering professional network
    • References
    • Appendix A: Proofs
    • Appendix B: Price simulation details
    • Appendix C: Hardware requirements for nodes
    • Appendix D: Performance evaluation data
  • Worker Node Guide
    • What is Worker Node
      • How do the Worker Node work
      • Worker Node Rewards
      • How to run Worker Node
      • What is the Worker Node License (NFT)
    • Worker Node Sales
      • Guide to Purchase Worker Nodes
      • Worker Node Sale Timeline
      • Node Supply, Price, Tiers and Purchase Caps
      • Guaranteed Node Buyback
      • How to get Node Whitelisted?
      • Smart Contract Addresses
      • User Discounts & Referral Program
      • Worker Node Purchase FAQ
      • ABKK Collaboration FAQ
  • Verifier Node Guide
    • What is Verifier Node
      • How do the Verifier Node work
      • Verifier Node Rewards
      • How to run Verifier Node
      • What is the Verifier Node License (NFT)
    • Verifier Node Sales
      • Guide to Purchase Verifier Nodes
      • Verifier Node Sale Timeline
      • Node Supply, Price, Tiers and Purchase Caps
      • Guaranteed Node Buyback
      • How to get Node Whitelisted?
      • Smart Contract Addresses
      • User Discounts & Referral Program
      • Verifier Node Purchase FAQ
      • Aethir Node Winners FAQ
  • Inferix MVP
    • Tutorial: MVP for designers & GPU owners
    • PoR MVP
  • Inferix Testnet 2 on Solana & IoTeX [ENDED]
    • Adding GPUs to the Network
      • For GPU providers
      • For GPU providers without funds
      • For users without GPUs
      • For Inferix Node Holders
    • Renting GPU Devices
    • User Revenue Calculation
      • Worker Rewards
      • Rental Revenue
      • Viewing Revenue
      • Claiming Rewards
    • GPU Staking & Unstaking
      • Staking Requirements
      • Unstaking GPUs
    • Guide to get tIFX tokens
    • Why choose Inferix DePIN GPU Solutions?
  • Inferix Testnet 1 on IoTeX [ENDED]
    • Inferix GPU Solutions
    • Adding GPUs to the Network
    • Renting GPU Devices
    • User Revenue Calculation
    • GPU Staking
    • Multiple options to participate in the Staking & Mining Program
    • Special airdrop for Inferix Node Holders! 🎉
    • Guide to get tIFX tokens
    • FAQ
  • Inferix Explorer
  • Team & Achievements
    • Our Story
    • Team
    • Member of Cohort 1 DePINSurf
    • Achievements
  • Community & Events
    • Events
    • Inferix Campaign: "ALLIANCE" (ENDED)
  • Terms of Service
    • Privacy Policy
    • Airdrop Terms of Service
Powered by GitBook
On this page
  1. Inferix Whitepaper
  2. Economic model
  3. Inferix bench and IBME

IB and IBM

Based on PoR, Inferix can assess the computing power of a Worker Node at a given time and uses a measurement unit called Inferix Bench (IB). When IB is multiplied by the node's working time, it results in Inferix Bench minutes, abbreviated as IBM. Thus, IBM is the metric used to measure the workload within the Inferix network.

To provide a quantitative perspective, 1 IB is defined as the average rendering capacity of a standard unit node with 2x RTX4090 GPUs, 32 GB of RAM, 1x Intel Core i9 CPU, SSD storage. This figure is updated daily using DAO mechanism, using benchmarks of a set of sample scenes on 10 nodes. The hardware specification of standard unit nodes and the sample scenes are also DAO-adjustable.

Assuming the average rendering time for one frame of a scene GGG in the sample set is TG0T^{0}_{G}TG0​, then it is not a fixed number, but is instead derived from the combined rendering capacity of the 101010 randomly selected standard unit nodes at the benchmarking time. The value of TG0T^{0}_{G}TG0​ is influenced by GPU, CPU, storage read/write speeds and network speeds at the time of benchmarking, though the variation is negligible.

To determine the IB of any given node nnn, Inferix sends render requests for scene GGG (randomly selected) to that node periodically. Assuming the average time it takes that node to render one frame in GGG is TGT_GTG​, the rendering power of nnn is defined by:

IB(n)≜TG0TG\text{IB}\left(n\right) \triangleq \frac{T^{0}_{G}}{T_G}IB(n)≜TG​TG0​​

Thus, the larger the TGT_GTG​, the smaller the IBn\text{IB}_nIBn​ value.

PreviousInferix bench and IBMENextIBME

Last updated 8 months ago