Foundation Models

Quantization (Model Quantization)

Quantization (model quantization) is the conversion of a neural network's weights and activations from high-precision floating point to lower-precision formats such as INT8, FP8, or 4-bit representations, shrinking memory footprint and accelerating inference. Post-training quantization converts a finished model directly, while quantization-aware training simulates low precision during training to preserve accuracy.

Why it matters for physical AI

Billion-parameter vision-language-action models only fit real-time control budgets on embedded hardware after aggressive quantization, making it a standard step in deploying robot foundation models onboard.