Quantisation specialists for your dedicated team.

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 ML specialists on your team, with accuracy validated per segment.

At Azendo, quantisation work sits with a 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 through Azendo add the skill at the next monthly cycle.