---
title: "Hire Quantisation Specialists to Shrink and Speed Models"
description: "Quantisation specialists who weigh precision trade-offs and calibrate models to run smaller and faster. A dedicated AI team in Thailand, managed by Azendo."
url: "https://azendo.co/skills/quantisation/"
---

1. [Home](https://azendo.co/)
2. [Skills](https://azendo.co/skills/)
3. Quantisation

# Quantisation specialists for your dedicated team.

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 speeds up inference.

## Where quantisation fits in a long-term product team.

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 your dedicated 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 needed, which is a deployment constraint. Establishing it early is part of the scoping described in how an assignment runs.

## Common quantisation use cases.

* 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.

## Adding quantisation skills to your team.

Optimisation work sits with the machine learning engineers on your team, with accuracy validated per segment.

At Azendo, quantisation work sits with a [dedicated MLOps team](https://azendo.co/services/dedicated-mlops-team/). Quantisation specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that [hire offshore developers](https://azendo.co/hire-a-dedicated-development-team-in-thailand/) through Azendo add the skill when the product calls for it.

## Roles that use quantisation

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

## Service areas that use quantisation

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

Capacity is agreed across your whole team, with one monthly fee, not per skill.

## Related in mlops and model delivery

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

## Tell us what you need quantisation for.

A service delivery manager will reply with the specialists we'd suggest, the monthly capacity and what the first month looks like.

[Browse all skills](https://azendo.co/skills/)