Verified90% confidenceFactExact time
The self-attention mechanism causes computational complexity to grow quadratically with sequence length, so expanding the context from 1,000 to 4,000 tokens increases the computation by 16 times.
6
Sources
90%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
AI Data Centers Competing for Power: Why U.S. Manufacturing Energy Costs Keep Soaring
hackernewshackernews7/7/2026
Related Claims
Unverified大语言模型的注意力机制计算复杂度与序列长度的平方成正比，处理200K token上下文的计算成本远超过处理50K token80% similarUnverified14倍的提升由多因子叠加而来：批处理贡献3-4倍、内存优化贡献1.5-2倍、会话复用与线程调优再叠加若干77% similarUnverifiedWhen resolution doubles, the attention computation the model must handle often grows fourfold or more, causing elapsed time to increase non-linearly.75% similarUnverifiedStandard attention's computation grows quadratically with sequence length, whereas the low-rank bottleneck decomposes the transformation into two steps: first compressing to r dimensions and then expanding.74% similar
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