SemiAnalysis: Kimi K3 significantly reduces KV (Key-Value) transmission bandwidth, but AI network demand will not shrink as a result.
6 hours ago
Independent semiconductor and AI research firm SemiAnalysis has published a report stating that while Kimi K3 uses KDA for roughly three-quarters of its network layers, cutting KV cache transmission bandwidth by up to 10 times compared to a full global attention model, this does not mean the AI network switch market will see a significant contraction. The Kimi K3 boasts 2.8 trillion parameters; even with MXFP4 precision, each forward computation requires around 1.5TB of HBM bandwidth. To maintain reasonable interaction speed while enabling profitable deployment, the model still needs high-bandwidth network connections such as GB300 NVL72 to link a large number of chips, and relies on WideEP for scaling services. WideEP distributes 896 expert models across multiple GPUs, performing token distribution and result merging twice per layer and per forward pass, with over 120 such operations required for a single forward computation. In contrast, KV cache transmission between pre-filling and decoding only occurs once per conversation round, meaning the bandwidth saved by KDA may be far less than the network scaling demands imposed by large-scale expert models. SemiAnalysis adds that more efficient attention mechanisms could also push context lengths from 1 million tokens to over 5 million tokens. Per Jevons’ paradox, efficiency improvements may expand the overall scale of AI usage, further increasing network requirements.
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