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NewsTopic · vLLM PD分离部署1 min read·2026-10-14

PyTorch Video: Jifei Wang and Mengxuan Li on vLLM PD Disaggregation

The official PyTorch channel published a video featuring Jifei Wang and Mengxuan Li, exploring vLLM Prefill-Decode (PD) disaggregation deployment on alternative AI accelerators using Llm-d. This resource is suitable for engineering teams focused on LLM inference optimization and non-NVIDIA hardware adaptation.

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2026-09-25 04:35 GMT+8

The official PyTorch YouTube channel published a technical sharing video on September 24, 2026, titled "PD Disaggregation VLLM Deployment on Alternative AI Accelerators Using Llm-d".

The episode is presented by Jifei Wang and Mengxuan Li. The content focuses on applying the Prefill-Decode (PD) disaggregation architecture in large language model inference and specifically explores how to deploy the vLLM framework on alternative AI accelerators using the Llm-d tool.

For AI infrastructure engineers exploring LLM inference optimization on non-mainstream hardware platforms or studying vLLM's underlying scheduling mechanisms, this video offers a practical perspective on this specific technology stack combination.

Sources

  • 原始出处youtube.com↗
Topic

vLLM PD分离部署

vLLM的PD(Prefill-Decode)分离部署旨在通过解耦大模型推理的两个阶段来优化延迟与资源利用率。该架构将计算密集的预填充(Prefill)与内存带宽敏感的解码(Decode)分配至不同实例,允许独立调优首字延迟(TTFT)和令…

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4 steps1 piecesUpdated 2026-10-15 02:35

How it got here

  1. 2026-07-16LMCache项目提交PR #4218,为Kubernetes Operator添加对vLLM PD分离部署的原生支持,简化了多节点环境下Prefill/Decode角色的配置注入。
  2. 2026-07-31vLLM Recipes仓库提交PR #719,发布GLM-5.1模型结合TileRT引擎进行PD分离部署的指南,在8xB300硬件上实现了约428 tokens/s的解码速度。
  3. 2026-08-11LMCache PR #4218合并,正式确立通过Operator管理vLLM PD分离集群的标准路径,解决了NIXL依赖包在容器化环境中的安装冲突问题。

What is still unsettled

  • 在大规模生产环境中,如何高效处理Prefill实例意外终止导致的KV缓存加载失败?目前主要依赖decode端的重算策略(recompute),但缺乏统一的自动恢复标准。
  • PD分离在短单轮任务中的性能开销是否抵消了其延迟优势?现有基准测试显示在低并发下PD可能比Colocated模式慢10%-18%,需更多高负载场景数据。
  • 跨异构硬件(如NVIDIA GPU与非NVIDIA加速器)的PD分离部署中,KV传输协议(如NIXL/Mooncake)的兼容性与性能瓶颈尚未完全明确。
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