What is Dense Transformer?
It is the standard Transformer architecture where all parameters are active at every step.
Overview
Dense Transformer refers to the traditional structure where all computational units in a layer of an artificial intelligence model work collectively for every input. As input data passes through the model, the entire network is triggered, which requires high computational power. As the model's capacity grows, the energy and time consumed increase proportionally.
How it works
After input texts are tokenized, they pass through all parameters of the model simultaneously via matrix multiplications. This process ensures a deep understanding of the data but consumes hardware resources heavily. Especially in large-scale models, this full participation creates a significant hardware cost.
Where it is used
This architecture is widely preferred in standard large language models and basic text generation systems.
Commonly confused with
It is frequently confused with the Mixture of Experts (MoE) architecture, where only the relevant part of the parameters is active.
Questions and answers
Why are Dense Transformer models slow?
Because the entire network must work simultaneously for every word or operation, it creates a heavy load on the hardware.
In which areas is it used?
It serves as a fundamental building block in general-purpose natural language processing tasks and standard artificial intelligence infrastructures.
Related terms
This explanation was written in plain language for TreScout with AI and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →