RoCE (RDMA over Converged Ethernet) implements RDMA semantics on Ethernet (RoCEv2 uses UDP/IP), so NICs can perform remote memory access with low CPU utilization over the same physical switches many enterprises already operate. Its objective is to deliver InfiniBand-like GPU communication economics—fast NCCL all-reduces, NVMe-oF, llm-d KV moves—without maintaining a separate InfiniBand fabric. RoCE requires lossless Ethernet behavior: Priority Flow Control (PFC), Explicit Congestion Notification (ECN), buffer tuning, and often dedicated traffic classes so RDMA traffic is not dropped under burst load.
NCCL (NVIDIA Collective Communications Library) implements collective operations—all-reduce, broadcast, reduce-scatter, all-gather, and others—optimized for NVIDIA GPUs across NVLink within a node and RDMA (InfiniBand or RoCE) across nodes. Its objective in AI is to make distributed training and multi-GPU inference (tensor parallelism) scale: gradient shards must merge every step; attention and MLP partitions must exchange activations with minimal latency. Frameworks (PyTorch DDP/FSDP, vLLM tensor parallel) call NCCL (or delegate to it) rather than hand-rolling socket code.
InfiniBand is a high-performance network fabric designed for datacenter and HPC clusters, natively supporting RDMA (Remote Direct Memory Access) with low latency, high bandwidth, and features such as adaptive routing and congestion control at the link layer. Its objective in AI is to connect many GPU servers so distributed training (gradient all-reduce via NCCL) and multi-node inference (tensor parallel, llm-d prefill/decode KV transfer) are not limited by TCP overhead on a CPU. InfiniBand NICs (e.g. NVIDIA ConnectX) present verbs APIs; subnets are managed with an Subnet Manager and partitioned for multi-tenant isolation.
