Unverified50% confidenceFactExact time
When the sequence length n increases from 128k to 1M, the number of elements in the attention matrix grows by about 64 times.
1
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50%
Confidence
Long-term
Relevance
11/2/2026
First Seen
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Related Claims
VerifiedAt a sequence length of 4096, the N×N attention matrix alone consumes approximately 64 MB of memory in FP16 precision.79% similarUnverified128K长度的序列所需的注意力计算量是4K长度序列的1024倍78% similarUnverified128K上下文窗口的注意力矩阵规模是32K窗口的16倍73% similarUnverifiedThe self-attention mechanism of the Transformer architecture has a computational complexity of O(n²), and when n grows from 8K to 128K, the theoretical computational load increases by 256 times.71% similarUnverifiedEven with INT8 quantization and optimized attention mechanisms, a GPU with 24GB of VRAM may still run out of memory when faced with a generation task at 2.5mp plus 12 seconds.67% similar
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