In a large language model (LLM), matrix multiplication is a core operation used to transform and combine data at nearly every layer—especially in embeddings, linear layers, and the attention mechanism.
What is matrix multiplication in this context?
Matrix multiplication in LLMs typically involves:
Input matrix: Represents a sequence of word embeddings or intermediate token representations (e.g., shape [sequence_length, embedding_dim]).
Weight matrix: Learned parameters used to project input data into another space (e.g., [embedding_dim, hidden_dim]).
Matrix multiplication is how LLMs transform and relate information across tokens. It enables the model to learn patterns, dependencies, and representations at scale.
