Kimi K3 vs DeepSeek: The KV Cache Architecture Debate
AccBalanced · x · 2026-07-20
Pushing back against the notion that Kimi K3's smaller KV Cache will boost DRAM/NAND demand, a recent tweet argues that this comparison is overly simplistic.
- DeepSeek 的瓶颈：KV size grows with the context in every layer, making KV search and retrieval the primary bottleneck for inference.
- Kimi K3 的优势：Kimi typically fetches only a few megabytes of data from the previous state layer, with a fixed size. While slow processing can drop token throughput, it remains unaffected by context length.
- 结论：Even with 2.8T total parameters requiring massive HBM and GPU support, K3's unique memory handling mechanism gives it a distinct competitive edge right now.
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