An educational web application that brings Key-Value Cache concepts to life

This interactive visualization tool was inspired by Sebastian Raschka's excellent article "Understanding and Coding the KV Cache in LLMs from Scratch". While Raschka provides the theoretical foundation and PyTorch implementation, this tool offers a visual, hands-on approach to understanding KV-Cache concepts.

Without KV-Cache, transformer models face O(n²) computational explosion during text generation:

• Token 1: 1 computation
• Token 2: 2 computations
• Token 3: 3 computations
• Token n: n computations
• Total: ~n²/2 operations

This makes real-time AI conversation impossible. KV-Cache transforms this to O(n) linear complexity.

• Token Processing Pipeline: Step-by-step token flow with interactive controls
• Computation Complexity Comparison: Side-by-side O(n²) vs O(n) matrix visualization
• Multi-Head Attention: 4 specialized attention heads (Syntax, Semantic, Position, Long-range)
• Real-time Cache Building: Watch key-value pairs accumulate in each head

• Step-by-step Processing: Process tokens one at a time or automatically
• Multiple Orchestration Modes:
  • Basic KV-Cache (standard behavior)
  • Shared Cache (with cache hit/miss simulation)
• Dynamic Explanations: Real-time descriptions of cache behavior

• Triangle vs Linear Pattern: Visual representation of computational complexity
• Cache Hit/Miss Simulation: Understanding shared cache efficiency
• Attention Weight Visualization: See how different heads focus on different patterns

kv_cache/
├── index.html                 # Main application entry
├── css/
│   ├── main.css              # Overall styling and layout
│   └── attention-head.css    # Individual head styling
└── js/
    ├── main.js               # Application orchestrator
    ├── AttentionHead.js      # Individual head logic
    ├── TokenPipeline.js      # Token processing and controls
    ├── OrchestrationManager.js # Coordination patterns
    └── ComputationComparison.js # Complexity visualization

1. Clone the repository

   git clone [your-repo-url]
   cd kv-cache-visualization

2. Open in browser

   # Simply open index.html in a modern browser
   # Or serve locally:
   python -m http.server 8000

3. Try the demo

   • Enter text like "AI is super cool"
   • Select different orchestration modes
   • Use "Step" to process tokens individually
   • Watch the complexity comparison in real-time

• Visual Learning: See abstract concepts in action
• Interactive Exploration: Control the pace and experiment with inputs
• Pattern Recognition: Understand O(n²) vs O(n) complexity visually

• Classroom Ready: No installation required, works in any browser
• Multiple Learning Styles: Visual, kinesthetic, and analytical approaches
• Scalable Content: From high school to graduate-level instruction

• System Design Insights: Understand memory-computation trade-offs
• Performance Implications: See why modern AI responds instantly
• Architecture Understanding: Grasp multi-head attention coordination

Together they provide: Complete understanding from concept to implementation.

• Attention Computation Explosion: Visual O(n²) growth pattern
• KV-Cache Optimization: Linear complexity solution
• Multi-Head Specialization: Different heads for different patterns
• Cache Management: Storage, retrieval, and hit/miss scenarios
• Memory-Speed Trade-offs: Why caching uses more memory but saves time

• ✅ Chrome/Edge (recommended)
• ✅ Firefox
• ✅ Safari
• 📱 Mobile responsive

Contributions welcome! Areas for enhancement:

• Additional orchestration patterns
• More detailed attention visualizations
• Performance metrics display
• Educational content expansion

MIT License - feel free to use for educational purposes.

• Sebastian Raschka for the foundational article and PyTorch implementation
• Transformer Architecture pioneers for the underlying concepts
• Open Source Community for the tools that made this possible

Learn by doing - Understanding KV-Cache through visualization makes the abstract concrete and the complex intuitive.