Technical Reference & User Guide

This documentation provides detailed specifications for our AI-generated art platform and neural network visualization system.

Core Technical Stack

TensorFlow & PyTorch

128-bit floating point training with distributed GPU optimization for large-scale neural network visualizations.

  • • 16-layer ResNet derivatives for pattern recognition
  • • Quantum-aware tensor operations for fractal synthesis

Quantum Simulation Engine

4096 qubit state simulation with tensor network reductions for real-time rendering.

  • • VQE (Variational Quantum Eigens Solver) for energy state optimization
  • • Qiskit integration for algorithm validation
API Endpoints
GET /art/preview/{id}

Returns low-res preview of artwork with metadata

  • • id (string): unique artwork identifier
  • • format (query): png, webp, json
POST /api/generate

Initiates a new artwork generation request

  • • style (int): neural/quantum/fractal
  • • resolution (query): 2048/4096/8192
  • • iterations (int): 5000-200000

System Requirements & Performance Considerations

Minimum System Requirements

GPU

NVIDIA A100 or equivalent with 32GB VRAM

CPU

AMD EPYC™ 7702 or Intel Xeon Gold 6330+

Memory

128GB DDR4 ECC RAM for render processing

Optimal Performance Metrics

4K Render Time

Typically 30-45 minutes on reference hardware

Includes color optimization and post-processing

Memory Usage

Typically 85-95% of 128GB DDR4 in active renders

Storage Requirements

750-1500GB for active rendering cache

SSD required for all 4K+/8K formats

Regulatory Compliance

EU-GDPR

Full compliance implemented for data minimization and user access rights

CCPA

Right to Opt-Out implemented via data preferences settings

CyLM Act

Age verification implemented for AI training data collection