How Transformer Architecture Works — Encoder, Decoder, Tokens, and Context

Short summary

Transformers process tokens in parallel via attention mechanisms instead of sequentially like RNNs, making them GPU-efficient and better at capturing long-range relationships. The encoder-decoder architecture: encoder builds contextual token representations; decoder generates output using masked self-attention. Attention-based processing is the core difference from older recurrent models.

• •Transformers compare all tokens directly in parallel, not left-to-right sequentially, enabling GPU optimization
• •Encoder reads input and builds context-aware token representations; decoder generates output with masked attention
• •Attention mechanism captures long-distance token relationships more effectively than RNN memory mechanisms

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