An AI model architecture specifies how computation is organized. It defines elements such as layers, attention mechanisms, expert modules, state representations and the paths through which inputs are transformed into outputs.
Architecture affects capability, efficiency and hardware behavior, but novelty is not the only route to improvement. Better data, objectives and training curricula can make an established architecture substantially more capable without changing its basic structural design.
ELI5
A model architecture is the structural plan for how an AI model processes information. It defines the model's main components, how they connect and the route data follows from input to output.
For example, one architecture may send every input through the same layers, while another activates only a few expert components for each token. Architecture affects speed and capability, but better data and training can also improve a model without changing its basic design.
How is model architecture different from model parameters?
Architecture defines the arrangement and operations of the model, while parameters are the learned numerical values used within that structure.
Does a better AI model always need a new architecture?
No. Improvements in data quality, training schedules, optimization and post-training can raise performance even when the underlying architecture remains familiar.
