本文探讨了大模型应用中PD分离部署的必要性，分析了Prefill与Decode阶段的资源需求差异，建议将两者部署在不同设备上以优化性能。同时介绍了vLLM的连接器和部署过程，强调了缓存共享与负载均衡的重要性。

DolphinGemma, a large language model developed by Google, is helping scientists study how dolphins communicate — and hopefully find out what they're saying, too.

DolphinGemma, a large language model developed by Google, is helping scientists study how dolphins communicate — and hopefully find out what they're saying, too.

DolphinGemma, a large language model developed by Google, is helping scientists study how dolphins communicate — and hopefully find out what they're saying, too.

DolphinGemma, a large language model developed by Google, is helping scientists study how dolphins communicate — and hopefully find out what they're saying, too.

DolphinGemma, a large language model developed by Google, is helping scientists study how dolphins communicate — and hopefully find out what they're saying, too.

DolphinGemma, a large language model developed by Google, is helping scientists study how dolphins communicate — and hopefully find out what they're saying, too.

DolphinGemma, a large language model developed by Google, is helping scientists study how dolphins communicate — and hopefully find out what they're saying, too.

DolphinGemma, a large language model developed by Google, is helping scientists study how dolphins communicate — and hopefully find out what they're saying, too.

LLM推理过程分为Prefill阶段和Decode阶段，Prefill阶段计算密集，Decode阶段生成token。评估指标为TTFT和TPOT，要求90%的请求的TTFT和TPOT值都小于等于0.4s和0.04s。PD分离优化了TTFT和TPOT指标，Prefill阶段限制Batch Size，Decode阶段增大Batch Size。

本研究介绍了DECODE，一种以频域序列建模为主的端到端模型，用于EMRI信号检测。DECODE能够高效处理一年的多通道TDI数据，在信噪比50到120之间实现96.3%的真阳性率和1%的假阳性率。DECODE展示了基于空间的引力波数据分析的潜力。