当大模型陷入“序列越长，算力越贵”的泥潭，DeepSeek R1的Multi Latent Attention技术却宣称能在10k+token任务中实现推理速度提升3倍、显存占用减少40%——是革命性突破，还是参数魔术？ 本期由AI主播“灵机”用10分钟为你拆解： * 动机：传统Attention如何沦为长文本的“算力黑洞”？ * 破局：Multi Latent Attention的“潜态注意力蒸馏”为何能打破序列长度诅咒？ * 实现：动态路由+分层压缩，低成本逼近全局注意力的技术密码。 While most LLMs drown in soaring compute costs as context grows, DeepSeek R1's Multi Latent Attention claims to slash GPU memory by 40% and boost speed 3x on 10k+ token tasks—revolution or parameter trickery? Join AI host "MindSpark" in this 10-minute deep dive: * Why It Matters: How vanilla Attention became a "compute black hole" for long texts. * The Breakthrough: "Latent Attention Distillation" – breaking the curse of sequence length. * Under the Hood: Dynamic routing + hierarchical compression – the cheap approximation of global attention. Bonus: How this could reshape the AI chip arms race and cloud pricing wars. 声明：本集所有内容为AI生成。 简介 by DeepSeek R1, 声音 by NotebookLM