--- title: A Guide to Multi-Node Tensor and Pipeline Parallelism description: Production LLM inference at scale requires distributing models across multiple GPUs and nodes. This guide addresses the practical engineering decision of how... canonical: https://daily.dev/posts/a-guide-to-multi-node-tensor-and-pipeline-parallelism-w8cxdynel twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: A Guide to Multi-Node Tensor and Pipeline Parallelism | daily.dev og:description: Production LLM inference at scale requires distributing models across multiple GPUs and nodes. This guide addresses the practical engineering decision of how... og:url: https://daily.dev/posts/a-guide-to-multi-node-tensor-and-pipeline-parallelism-w8cxdynel og:image: https://api.daily.dev/og/posts/w8cXDYNEL.png og:image:alt: A Guide to Multi-Node Tensor and Pipeline Parallelism og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # A Guide to Multi-Node Tensor and Pipeline Parallelism **[DigitalOcean Community](https://daily.dev/sources/do_community)** · 14 min read · 0 upvotes · 0 comments ## Summary Production LLM inference at scale requires distributing models across multiple GPUs and nodes. This guide addresses the practical engineering decision of how to choose tensor parallelism (TP), pipeline parallelism (PP), and data parallelism (DP) degrees given model size, hardware topology, latency SLA, and traffic patterns. Key insights: TP should generally stay within the fastest local GPU interconnect domain (e.g., NVSwitch within a node) because cross-node collectives add latency at every decode step. PP is better suited for spanning nodes since it only transfers activations at stage boundaries. DP should be added once per-replica topology is efficient. A decision table covers common deployment scenarios from single-node fits to large MoE models. The guide also covers how serving engines (vLLM, Ray Serve, SGLang, Megatron) expose these parallelism knobs and how network fabric capabilities constrain which layouts are viable. ## Full article daily.dev links to this article rather than hosting it. 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