An artificial intelligence model embedding layer maps each token or input unit to a learned vector. Those vectors provide the continuous numerical representation consumed by later model layers.
A large vocabulary or embedding dimension can make the component consume substantial memory even when it does not dominate per-token arithmetic. Some local runtimes can place the embedding weights in system memory to preserve accelerator capacity for other layers.
Acronyms and aliases
AI model embedding layer acronymtoken embedding layer synonymartificial intelligence model embedding layer variant
Related terms
Frequently asked questions
What does an embedding layer do with a text token?
It looks up a learned numerical vector for the token, giving later model layers a representation they can transform.
Why can a model embedding layer be memory-heavy?
It stores a vector for every supported token or input unit, so a large vocabulary and wide vectors create many parameters.
