Home▸AI & Machine Learning▸Neural Network Quantization Explained

🧮 Neural Network Quantization Explained

A 3D weight-matrix that snaps from 32-bit floats down to low-bit integers live, showing the quantization error, memory savings and compression ratio as you change bit depth.

AI & Machine Learning3DAdvanced60 FPS

A grid of glowing bars represents a slice of a neural network's weight matrix. Each bar's true 32-bit value hovers as a translucent wafer; lower the bit width and watch the bars snap onto a coarser grid of integer levels while the gap to the true value — the quantization error — grows.

🔬 What It Demonstrates

Affine quantization maps a continuous range of float weights onto a small number of evenly spaced integers using a scale (and optional zero-point). Fewer bits mean fewer levels, more rounding, and higher error — but far less memory and faster integer math.

🎮 How to Use

Drag the bit-width slider from 8 down to 1 and watch the bars quantize. Switch weight distributions to see how outliers wreck low-bit accuracy, try per-channel scaling to fix it, and toggle the FP32 baseline to compare against full precision.

💡 Did You Know?

INT8 quantization is the default for deploying large models on phones and edge devices — it shrinks a model to roughly a quarter of its FP32 size with often under 1% accuracy loss.

⚙ Under the hood

A 3D weight-matrix that snaps from 32-bit floats down to low-bit integers live, showing the quantization error, memory savings and compression ratio as you change bit depth.

neural networksquantizationmachine learningdeep learningoptimizationbit depthThree.js

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