Infrastructure & Deployment

Model Compression

Techniques to make large models smaller so they run on less powerful hardware.

Definition

Model compression covers a family of techniques — quantisation, pruning, knowledge distillation, and others — that reduce the size and resource requirements of neural networks. Compressed models run faster, cost less to serve, and can be deployed on edge devices or in low-resource environments. The trade-off is some reduction in capability, though carefully compressed models often retain the vast majority of their performance for specific tasks.

Related Terms

Quantisation

Reducing a model's numerical precision to make it smaller and faster, with minimal quality loss.

Distillation (Knowledge Distillation)

Training a smaller model to mimic a larger, more capable one.

Edge AI

Running AI models directly on local devices rather than in the cloud.

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Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Infrastructure & Deployment·Model Compression