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Primary Use Cases

Centrix enables cost-effective, scalable computing across multiple industries:
  1. CGI Rendering - Film, TV, and game rendering at massive scale
  2. AI/ML Training - Cost-effective model training and fine-tuning
  3. Scientific Computing - Research simulations and data analysis
  4. Blockchain Infrastructure - Node operation and smart contract indexing

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CGI Rendering

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Market Size

$15B globally - Films, games, architecture, product visualization

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Why Centrix is Ideal

Cost Savings: 70-90% cheaper than centralized render farms Scale Instantly: Access 100,000+ GPU hours on demand Global Distribution: Render from servers closest to studios Pay-Per-Frame: Only pay for frames actually rendered

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Example Workflow

Step 1: Studio Setup
  1. Studio uploads scene file to Centrix
  2. Specifies render quality and deadline
  3. Sets budget (e.g., $5,000 for full frame)
  4. System splits into optimal render chunks
Step 2: Distribution
  1. 10,000 frames distributed to idle GPUs worldwide
  2. Providers bid on chunks based on their hardware
  3. Studio accepts optimal price/speed combo
  4. Rendering begins immediately
Step 3: Verification & Delivery
  1. Providers upload rendered frames
  2. Spot-check verification on 5-10% of frames
  3. Automatic quality assurance (artifacts, color grading)
  4. Studio receives final output in 24-48 hours

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Impact Example

Animation Studio with 100,000-frame project:
  • Traditional render farm: 100 hours on dedicated hardware = $50,000
  • Centrix: 12 hours distributed = 5,000(cost)+5,000 (cost) + 500 (fees)
  • Result: 10x cost reduction + 8x speed improvement

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AI/ML Training

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Market Size

$90B globally - Growing 40% annually as AI adoption spreads

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Why Centrix is Ideal

Hyperscaling: Scale from 1 GPU to 10,000 GPUs instantly Cost Optimization: Pay market rates, not enterprise premiums Flexibility: Mix GPU types (H100, A100, RTX) for optimal cost/speed Privacy: Keep proprietary models on Centrix network only

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Example: Model Training

Scenario: Startup training a 7B parameter LLM Traditional (AWS):
  • 8x NVIDIA H100 GPUs: 12/hour=12/hour = 28,800/day
  • Training time: 30 days = $864,000
  • Setup/management overhead: $50,000
Centrix:
  • Distributed training on heterogeneous providers: $3.50/hour average
  • Same 30-day training: $2,520
  • Zero setup overhead, automatic optimization
Result: 99.7% cost reduction

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Use Case: Hyperparameter Optimization

Research teams often run 100+ training experiments to find optimal parameters. 
Traditional: 100 × $1,000 = $100,000
Centrix: 100 × $10 = $1,000
Cost reduction: 99\%

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Scientific Computing

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Applications

Centrix enables breakthrough research across multiple scientific domains:
  • Climate Modeling - Weather prediction and climate scenarios
  • Genomics - Genetic sequencing and protein folding
  • Particle Physics - Simulations and data analysis
  • Drug Discovery - Molecular dynamics simulations
  • Astrophysics - N-body simulations and data processing
  • Materials Science - Crystal structure and property analysis

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Market Impact

Current Bottleneck: Researchers lack compute access
  • Academic computing budgets: $50K-500K/year
  • HPC cluster time: Highly competitive, long queues
  • Cloud: Too expensive for extended research
Centrix Solution:
  • Democratizes access to supercomputing power
  • Accelerates research timeline by 5-10x
  • Enables breakthrough discoveries from resource-constrained labs

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Example: Protein Folding Project

Traditional HPC:
  • 3-month queue wait
  • 6-month computation
  • $200,000 cost
  • Total: 9 months
Centrix Network:
  • Instant access
  • 2-week computation
  • $20,000 cost
  • Total: 2 weeks
Result: 18x faster, 90% cost reduction

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Blockchain Infrastructure

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Applications

Full Node Operation
  • Running Ethereum/Polygon/other chain validators and nodes
  • Earn staking rewards + Centrix compute payments
  • Providers profitable even if staking returns modest
Blockchain Indexing
  • Processing and indexing blockchain data for applications
  • The Graph style queries and subgraph support
  • Real-time blockchain state queries
MEV Extraction
  • Maximal Extractable Value calculations and market-making
  • Competitive bidding ensures fair MEV distribution
  • Programmable verification of MEV strategies
Smart Contract Testing
  • Formal verification and property testing at scale
  • Automated contract auditing
  • Fuzzing and security analysis

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Economic Model

Node Operator Earnings:
  • Staking rewards: 4-8% annually
  • Centrix compute payments: $50-200/month per node
  • Total: 12-15% annual yield vs. 4% on centralized nodes

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Emerging Use Cases

Additional applications gaining traction:
  • Video Encoding - Transcode millions of videos to multiple formats
  • Data Analytics - Process petabytes of data for analytics
  • Web3 Infrastructure - IPFS storage, blockchain archives, indexing
  • Real-Time Processing - Stream processing and live event rendering

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Market Opportunity Summary

MarketSizeCentrix TAMTimeline
CGI Rendering$15B$2.5B2-3 years
AI/ML Training$90B$10B3-4 years
Scientific Computing$50B$5B2-3 years
Blockchain$20B$3B1-2 years
Other Computing$100B+$5B+4+ years
Total addressable market: $25.5B+ with potential for rapid scaling as adoption grows.