Woofun AI notes that SemiAnalysis reports Kimi K3 reduces KV cache transfer bandwidth by up to 10 times through KDA usage. Despite this efficiency, the model's 2.8 trillion parameters require 1.5TB of HBM bandwidth per forward computation. Profitable deployment necessitates high-bandwidth connections like GB300 NVL72 and WideEP services. WideEP distributes 896 expert models across GPUs, executing token distribution and result merging twice per layer. This process demands over 120 executions per forward computation, outweighing bandwidth savings from reduced KV cache transfers. SemiAnalysis suggests efficient attention mechanisms may extend context lengths from 1 million to over 5 million Tokens. Under Jevons Paradox, such efficiency gains could expand AI usage scale, further increasing network demands.