Transformer Architecture Deep Dive

Every LLM API call runs transformer blocks. Knowing how attention and the decode loop work explains why TTFT differs from inter-token latency, why KV-cache sizing is a hard GPU memory constraint, why long contexts are expensive, and why tool calls can fail for non-model reasons. This mental model prevents a whole class of wrong diagnoses and over-engineered fixes.

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