Verified75% confidenceFactExact time
KV Cache reduces the time complexity of Transformer incremental generation from O(L²) to O(L).
5
Sources
75%
Confidence
Long-term
Relevance
7/8/2026
First Seen
Sources
GPT-5.6 Ultra子Agent架构解析与AI行业十大动向
bilibili竹言见智6/29/2026
Related Claims
VerifiedKV Cache将已计算的历史键值矩阵缓存在内存中，将自回归生成的复杂度从随序列长度平方增长降至线性73% similarUnverifiedIn traditional Transformer inference, the KV Cache for each request requires pre-allocation of contiguous GPU memory; because sequence length is unpredictable, allocation is often done at maximum length, leading to memory fragmentation and waste, with actual utilization sometimes below 50%.71% similarUnverifiedSet Transformer reduces complexity to O(mn) by introducing learnable Inducing Points.69% similarVerifiedThe KV cache grows linearly with context length, increasing the memory read volume per step in the decoding phase, which is the fundamental reason decoding speed decreases as context grows.69% similarUnverifiedWithout caching K and V, every generation requires recomputing the entire context, with a time complexity of O(n²).68% similar
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