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. Sep…