DeepSeek新论文提出DualPath推理系统,智能体负载吞吐近翻倍

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PANews 2月27日消息,在业界对新一代旗舰模型DeepSeek V4的翘首期盼中,DeepSeek团队却悄然放出了一篇新的学术论文。新论文介绍了一个名为DualPath的创新推理系统,专门针对智能体工作负载下的大模型(LLM)推理性能进行优化。通过引入“双路径读取KV-Cache(类似记忆缓存)”机制,重新分配存储网络负载,将离线推理吞吐量最高提升1.87倍,在线服务的每秒智能体运行数平均提升1.96倍。论文在引言部分提到,大模型正从单轮对话机器人和独立推理模型,快速演进为智能体系统 ——能够自主规划、调用工具,并通过多轮交互解决实际任务。这种应用范式的转变,推动大模型推理工作负载发生重大变革:从传统的人类-大模型交互,转向人类-大模型-环境交互,交互轮次可达数十甚至数百轮。

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