Recent infrastructure analyses highlight a critical intersection between distributed software strategies and physical GPU interconnect topologies. As foundation models exceed single-GPU memory limits, practitioners increasingly rely on parallelization frameworks to distribute workloads, but performance outcomes now depend heavily on the underlying hardware fabric. Distributed training primarily operates through three software paradigms: Distributed Data Parallel, Fully Sharded Data Parallel, and Microsoft DeepSpeed ZeRO stages. Distributed Data Parallel replicates the entire model state across all GPUs, dividing only the data batch. This approach minimizes communication overhead and maximizes throughput when model weights, gradients, and optimizer states fit within available VRAM. However, it fails for models exceeding single-GPU capacity. Fully Sharded Data Parallel and ZeRO address this memory constraint by sharding model components across devices. ZeRO offers incremental stages that progressively offload optimizer states, gradients, and finally parameters, trading increasing communication volume for reduced memory footprint. While theoretically sound, these sharded approaches generate frequent collective operations that can become severe bottlenecks depending on network speed. Experimental testing on A100 and H200 GPUs confirms that software strategy alone does not dictate performance. The physical wiring between GPUs fundamentally alters throughput. Standard Peripheral Component Interconnect Express buses route traffic through the CPU and host memory, offering limited bandwidth but introducing significant latency. NVIDIA NVLink provides direct GPU-to-GPU pathways reaching 450 gigabytes per second, but implementation varies. NVL configurations use physical bridges to link GPUs in small groups, relying on slower system paths for cross-group communication. Conversely, NVSwitch architectures, typical of Server Express Module form factors, create an all-to-all fabric where every GPU communicates at full NVLink speed. Benchmarks reveal that the same distributed training code can experience performance variations of up to tenfold solely due to topology differences. On NVSwitch systems, Fully Sharded Data Parallel and ZeRO-3 achieve throughput comparable to Distributed Data Parallel while utilizing a fraction of the memory, effectively eliminating the traditional speed penalty for sharding. In NVL environments, strategic job placement is essential. Tasks confined to a single bridged group maintain high speeds, but workloads spanning multiple groups drop to system path speeds, causing throughput collapses of three to five times. Communication-heavy strategies suffer most on slow wires, as per-layer data transfers cannot be hidden behind computational work. The findings establish a clear operational principle: software determines the volume of data movement, while hardware determines the cost of that movement. Artificial intelligence infrastructure planning must therefore prioritize topology verification before workload deployment. Practitioners should routinely execute system topology checks to identify available interconnects. On fast all-to-all fabrics, Fully Sharded Data Parallel serves as a reliable default. On grouped bridge configurations, restricting jobs to single groups preserves performance, while cross-group workloads require conservative strategy selection. As models continue to scale, aligning distributed training frameworks with physical interconnect capabilities will remain essential for maximizing compute efficiency and reducing operational costs.