Interconnects for large-scale LLM training and inference: RDMA, InfiniBand, NVLink, and the network stack
What does the evidence from 2020 to 2026 show about how interconnect technologies (RDMA, InfiniBand, NVLink, Ethernet/RoCE, and the software stacks built on them) shape the performance, scaling, and cost of large-scale LLM training and inference?
Large-scale LLM training and inference now depend on a hierarchy of interconnects: NVLink and NVSwitch inside servers, InfiniBand or RoCE Ethernet between them, and collective-communication libraries that turn raw fabric into training progress. The 2020-2026 evidence shows the lossless RDMA fabric that carried early GPT-era training is under strain, with congestion-control pathologies and head-of-line blocking documented in both directions, while production operators engineer around it and a lossy, packet-spraying Ultra Ethernet path is emerging. Meanwhile Mixture-of-Experts models and long-context inference have made all-to-all traffic and KV-cache movement the new communication bottlenecks, and NVLink scale-up domains, UALink, and CXL memory pooling are reshaping where computation and memory live. Confidence is moderate: the transport and collective stories are replicated across many independent studies, but the newest hardware (NVLink 4/5, UALink, GB200-class superpods) is covered mainly by vendor-adjacent or preprint evidence.
Updated 8 Aug 2026137 sources2020–2026Deep23 min read
RDMA · InfiniBand · NVLink · NVSwitch · RoCE · Ultra Ethernet · UALink · NCCL · collective communication · all-to-all · Mixture-of-Experts · KV cache · CXL · optical circuit switching · LLM training · LLM inference