In modern GPU-centric clusters, large-scale AI training relies on two distinct communication domains: a high-bandwidth intra-node domain using proprietary interconnects (e.g., NVLink), and a scale-out inter-node network domain (e.g., RDMA). We observe that the widely-used ring algorithm, often create a significant load imbalance across these domains. This leads to the counter-intuitive scenario where the expensive, high-bandwidth intra-node domain becomes a performance bottleneck, while the inter-node network remains underutilized. This inefficiency is further exacerbated by the disparity in bandwidth provisioning: inter-node network bandwidth is generally more cost-effective and accessible, whereas intra-node bandwidth is often proprietary and more costly to scale. To address this fundamental imbalance, we propose RAIL, aimed at resolving the intra-node bottleneck by strategically rearranging inter-GPU communication paths. This rebalancing ensures that traffic loads are appropriately matched with the distinct transmission capabilities of each domain, thereby maximizing overall communication performance. RAIL incorporates a Load Distributing Strategy (LDS) that can accurately partition physical nodes into logical nodes based on the a transmission capabilities of both domains, shifting excess traffic from the overloaded intra-node domain to the underutilized network domain. Additionally, the Intra-Rail Strategy (IRS) leverages topological characteristics to ensure optimal communication paths through the network domain between logical nodes. Our evaluation demonstrates that RAIL effectively mitigates congestion and achieves a 30.7% average increase in collective communication bus bandwidth compared to the widely-used NCCL solution.