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🧮 Neural Network Quantization: Shrinking Models for Edge AI

Explore how neural network quantization compresses 32-bit models into 8-bit integers, cutting memory and power use so AI can run directly on phones and edge devices.

Machine Learning & Neural Networks3DModerate60 FPS

The simulation visualizes a layer of floating-point weights being mapped onto a discrete integer grid, showing how scale and zero-point compress the continuous distribution into quantization buckets.

🔬 What It Demonstrates

The simulation visualizes a layer of floating-point weights being mapped onto a discrete integer grid, showing how scale and zero-point compress the continuous distribution into quantization buckets.

🎮 How to Use

Select a bit-width to see how the number of available integer levels changes, then use the speed slider and play/pause to animate weights snapping onto the quantized grid, or rebuild to generate a new random weight distribution.

💡 Did You Know?

Google's TensorFlow Lite and Apple's Core ML both default to INT8 quantization for on-device models, shrinking many networks to roughly a quarter of their original size while running several times faster on mobile hardware.

⚙ Under the hood

Interactive 3D lab where floating-point neural network weights snap onto discrete integer quantization levels, visualizing bit-width trade-offs for edge AI.

quantizationedge-aimodel-compressionint8neural-networkson-device-ai

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install