How Self-Attention Works — QKV, Softmax, and Matrix Computation

Short summary

Self-Attention computes token-to-token relationships through Query-Key-Value projections and softmax-weighted mixing. The core operation—Attention(Q,K,V) = softmax(QK^T/√d_k)V—executes as parallel dense matrix ops, enabling GPU efficiency but O(n²) memory scaling.

• •QKV projection creates three learned views of each token for comparison and value transfer
• •Similarity scores are scaled and softmaxed into attention weights governing information mixing
• •Matrix form enables parallelization across all tokens simultaneously, not sequentially

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