Unverified60% confidenceFactTime unknown
The KV Cache of Transformers grows quadratically with sequence length, leading to non-linear GPU memory consumption during long-context inference.
2
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60%
Confidence
Long-term
Relevance
5/31/2026
First Seen
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UnverifiedThe state update of recurrent networks is recursive, with serial dependencies between time steps, making it hard for GPU parallel efficiency to compete with Transformer, and this bottleneck may become more pronounced as model scale grows.77% similarVerifiedTransformer架构的自注意力机制在处理长序列时面临二次方复杂度的计算开销,上下文长度翻倍则计算量增长四倍75% 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%.73% similarUnverifiedThe computational cost of the Transformer self-attention mechanism grows quadratically with context length, and the "Lost in the Middle" effect causes the model to extract information from the middle of the window less effectively than from the beginning and end.72% similarUnverifiedResearch (Dettmers et al., 2022) has shown that specific hidden dimensions in Transformer models consistently exhibit abnormally large activation values across all tokens and layers, with the phenomenon becoming more pronounced in models exceeding 6.7B parameters.70% similar
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