Unverified50% confidenceFactExact time
The model uses a bidirectional attention encoder architecture, completing inference in a single forward pass, with constant-level latency that does not grow linearly with content length.
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50%
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Long-term
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9/25/2026
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Unverified在推理阶段输入内容通过并行化注意力机制一次性处理,而输出内容需自回归逐Token生成,无法并行化,构成大模型推理延迟的根本瓶颈78% similarUnverifiedWhen resolution doubles, the attention computation the model must handle often grows fourfold or more, causing elapsed time to increase non-linearly.76% similarUnverified在推理阶段内存带宽往往比纯算力更早成为瓶颈,注意力机制需在每步生成时读取全部KV缓存,上下文长度增大时内存读写开销呈平方级增长74% similarUnverifiedThe self-attention mechanism resolves the vanishing gradient problem that RNN architectures face when processing long sequences and inherently supports large-scale parallel computation73% similar
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