Quantisation
Quantisation makes AI models lighter and faster by reducing precision, helping translation and localisation teams run capable systems on practical hardware.
Definition
A model optimisation technique that reduces the numerical precision of neural network parameters to decrease memory usage and improve inference speed.
How It Works
Quantisation helps teams build predictable AI and translation workflows by setting clear expectations for quality, consistency, and decision-making.
In production environments, this concept is applied with process controls such as human review, terminology alignment, and repeatable quality checks across multilingual content.
Quantisation is most valuable when it improves speed and memory efficiency without compromising the quality standards required for production translation.
Key Concepts
- core principle of quantisation
- workflow-level implementation
- terminology and quality consistency
- human validation before publication
Where It Is Used
- localisation workflows
- AI translation pipelines
- multilingual content production
- cross-referencing related concepts such as Quality Assurance (QA)