Since 2017, attention has been redesigned again and again to make decode cheaper. The changes pull on two different levers. Store less per past token: MHA (2017): every head keeps its own K and V. MQA (2019): all heads share one K and V. GQA (2023): groups of heads share one. Llama 3 uses this. MLA (2024): one short learned code per token. Shared weights let the heads use it without rebuilding past K and V. DeepSeek V2 and V3. Read fewer past tokens: Sparse attention (2025): a small index scans the past, and the main attention reads only the tokens it picks. DeepSeek V3.2, built on top of MLA. KV reads have often constrained decoding. These designs trade storage, memory traffic, and computation in different ways. MLA shows that trade clearly. The card shows the cache shrinking 57x per layer against same-size MHA, while the math per byte goes up. By my rough math, that puts MLA decode on an H800 close to where compute, not memory, becomes the limit. A third lever is fewer sequential generation steps: speculative decoding, multi-token prediction, and, for tasks that need a decision rather than prose, models like Jev that return a typed answer with probabilities instead of writing text.