Unverified50% confidenceFactExact time
The attention matrix size required to process a 128K token sequence is 256 times that of an 8K token sequence.
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50%
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Long-term
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9/4/2026
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VerifiedFor a typical 7B model with 32 layers and 32 attention heads, storing the KV Cache for each token requires approximately 0.5 MB of VRAM, so generating 1,000 tokens requires an additional ~500 MB of VRAM74% similarUnverified主流BPE词表规模通常为32K-128K，全量成对相似度矩阵的内存占用可超过数十GB74% similarUnverifiedA 200K token context request requires approximately 25× the KV-Cache memory compared to an 8K token request.72% similarUnverifiedThe computational complexity of liquid convolution scales linearly with sequence length at O(n), rather than the quadratic O(n²) of self-attention, resulting in approximately 1/1000 the computation of attention layers when processing 128K tokens.72% similarUnverified128K指模型的上下文窗口大小，即模型单次可以处理约128,000个Token的输入信息71% similar
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