Investor Guide

Understanding Returns

How Returns Are Generated#

Revenue Sources#

1. AI Model Training#

Clients pay hourly rates for GPU access to train:

  • Large language models (LLMs)
  • Computer vision models
  • Recommendation systems
  • Reinforcement learning agents

Typical rate: $2–8 per GPU hour

2. AI Inference Serving#

Real-time model serving for:

  • API endpoints
  • Production applications
  • High-frequency inference
  • Batch processing

Typical rate: $1.50–5 per GPU hour

3. 3D Rendering & Graphics#

Studios and creators use cluster capacity for:

  • Movie VFX and animation
  • Architectural visualization
  • Product rendering
  • Real-time graphics

Typical rate: $1–4 per GPU hour

4. Scientific Computing#

Research institutions run workloads such as:

  • Molecular dynamics simulations
  • Climate modeling
  • Genomic analysis
  • Financial modeling

Typical rate: $2–6 per GPU hour

5. Blockchain & Web3#

Computational tasks may include:

  • Zero-knowledge proof generation
  • Consensus validation
  • Cryptographic operations
  • Decentralized network support

Typical rate: $1.50–4 per GPU hour

Return Calculation Example#

Scenario: Enterprise Package#

  • Your investment: $1,299
  • Cluster: H100 ×4 configuration
  • Rental period: 65 days

Daily Revenue Breakdown#

  • Cluster charges B2B clients: $4/hour average
  • Cluster operates: 20 hours/day average (83% utilization)
  • Daily B2B revenue: $320 ($4 × 4 GPUs × 20 hours)
  • Platform operational costs: 30% ($96)
  • Net distributable revenue: $224
  • Your share: $22.40 as 1 of 10 node investors
  • Your daily return: 1.72% ($22.40 / $1,299)

Over 65 Days#

  • Total accumulated: $1,456 ($22.40 × 65)
  • Profit: $157 ($1,456 - $1,299)
  • ROI: 12.1% over 65 days
  • Annualized: approximately 68%, if similar performance is maintained

Important Notes#

  • Actual returns vary based on real cluster utilization
  • Some days may be higher or lower than the advertised range
  • Weekends and holidays may produce lower demand
  • Peak seasons, such as Q4 for rendering and year-round AI demand, may produce higher utilization

Factors Affecting Returns#

Positive Factors (Higher Returns)#

  • High B2B client demand
  • Premium client contracts
  • Extended job durations
  • Peak-season workloads
  • New cluster deployments
  • Geographic demand surges

Negative Factors (Lower Returns)#

  • Reduced B2B bookings
  • Seasonal slowdowns
  • Technical maintenance windows
  • Market competition
  • Hardware issues, even when covered by SLA processes

Return Stability#

Historical performance data shows:

  • 80–90% of days fall within the advertised range
  • 5–10% of days exceed the high end
  • 5–10% of days fall below the low end
  • Average utilization is 85% across all clusters