---
title: "quantisation | Skills We Assign For | Azendo"
description: "Model quantisation — precision trade-offs, calibration, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/quantisation/"
---

[Skills](https://azendo.co/skills/) MLOps and model delivery 

# quantisation.

Quantisation reduces the numerical precision of model weights and activations — from 32-bit floats to 16-bit, 8-bit integers or lower — which shrinks memory use and increases inference speed.

## Where quantisation fits on a long engagement.

The gains are large and mostly free at moderate reduction. Half precision typically costs nothing measurable in accuracy while halving memory, and 8-bit integer quantisation is often acceptable with calibration. Below that, degradation becomes real and model-dependent.

Accuracy loss is not uniform across inputs. A quantised model may perform identically on typical cases and noticeably worse on rare or difficult ones, so an aggregate accuracy comparison can hide a regression exactly where it matters most.

## What an assigned team does with quantisation.

Post-training quantisation is quick and sometimes insufficient; quantisation-aware training gives better results at low precision but requires retraining.

Choosing between them depends on how much reduction is actually needed, which is a deployment constraint rather than a modelling preference. Establishing it early is part of the scoping described in [how an assignment runs](https://azendo.co/how-it-works/).

## What we use quantisation for.

* Memory halved at no measurable cost Half precision where the accuracy difference is not detectable.
* Accuracy checked on hard cases Per-segment comparison, because aggregate scores hide tail regressions.
* Deployment constraints established first The required reduction known before choosing post-training or aware quantisation.

## How quantisation capacity is assigned.

Optimisation work is assigned inside AI capacity, with accuracy validated per segment rather than in aggregate.

## Roles we assign quantisation for

* [Model Deployment Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/model-deployment-engineer/)

## Service lines it sits in

* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in mlops and model delivery

* [TensorRT — skill we assign for](https://azendo.co/skills/tensorrt/)
* [Model registry — skill we assign for](https://azendo.co/skills/model-registry/)
* [Weights & Biases — skill we assign for](https://azendo.co/skills/weights-and-biases/)
* [Comet — skill we assign for](https://azendo.co/skills/comet/)
* [Neptune.ai — skill we assign for](https://azendo.co/skills/neptune-ai/)
* [DVC — skill we assign for](https://azendo.co/skills/dvc/)
* [Feast — skill we assign for](https://azendo.co/skills/feast/)
* [feature stores — skill we assign for](https://azendo.co/skills/feature-stores/)

## Tell us what your roadmap needs quantisation for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)