# Quantization In ML, quantization means converting real-valued parameters (weights, activations, etc.)—which are usually represented as 32-bit or 16-bit floating-point numbers—into lower-precision integer or fixed-point representations (like 8-bit integers). This is done to reduce memory and bandwidth costs and speed up inference, which also means speeding up proving inference. Plus, a floating-point model can’t be realistically proven inside a ZKP; encoding floats directly would explode circuit size and complexity The downside of quantization is that it may degrade the accuracy of the model. However, the loss of accuracy may be considered insignificant, as described in , > "Most existing models are trained with 32-bit floating points (FP32), which provides greater precision than needed. Model pruning and quantization techniques have been developed to address these issues by transforming dense, high-precision parameters (e.g., FP32) into sparse, lower-bit representations (e.g., 8-bit integers, INT8)." Currently, our models are quantized to 8-bits integers. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.icme.io/documentation/learning/jolt-atlas-zkml/onnx/quantization.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.