AI Glossary
Weight Sharing
What is Weight Sharing?
A neural network design technique in which multiple parts of a model share the same parameter values — reducing total parameter count without reducing network capacity for certain tasks. Weight sharing is used in convolutional neural networks and some efficient transformer variants. It is part of the broader toolkit for building smaller, more efficient models.
Example in practice
An engineer building an AI feature for mobile deployment would use architectures incorporating weight sharing to produce a smaller model footprint — reducing VRAM and compute requirements for on-device inference.
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