DFlash：让LLM无损加速快6倍

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DFlash 是一种创新的推测解码框架，旨在通过轻量化块扩散模型解决大型语言模型推理速度慢的问题。该方案利用主模型的隐藏层特征作为上下文引导，通过单次前向传递并行生成多个备选词元，显著降低了生成延迟。与传统的递归式草图模型相比，这种并行扩散采样方式极大提高了硬件利用率和草图准确性。实验证明，该技术能实现超过 6 倍的无损加速，且性能大幅领先于现有的 EAGLE-3 等前沿方法。这种将扩散模型定位为高效“草图员”的设计，为加速 AI 模型的实际落地提供了新范式。

Episode metadata supplied by the publisher feed · Published May 2, 2026

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DFlash：让LLM无损加速快6倍

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