Evaluating Multi-Node LLM Inference Orchestration Topologies at Scale

Multi-node LLM inference requires balancing network interconnect latency against memory bandwidth limits across distributed GPUs. While peer-to-peer orchestrators eliminate manual cluster setup, attempting tensor parallelism over standard Ethernet introduces massive synchronization delays unless low-latency RDMA or high-speed links are present.

For high-concurrency multi-turn workloads, disaggregated prefill and decode topologies paired with KV-aware routing offer far higher predictability. Separating compute-heavy prompt ingestion from memory-bound token generation prevents heavy requests from choking active streams, keeping context reuse high across multi-turn AI workflows.

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