Kimi Delta Attention unwrapped: why this tweak matters for LLM speed
The blog explains KDA mechanism from first principles, showing it as a natural extension of linear attention variants that replace costly softmax with a fixed-size state. It walks through the derivation from quadratic attention to the final Kimi Delta Attention equations and then reveals the Triton‑based recurrent and chunkwise implementations that slash inference memory and speed up large language models.