Which NVIDIA technology should a company use for efficient communication between GPUs during large-scale AI training?

Using NVIDIA's Collective Communication Library (NCCL) is vital for efficient communication among GPUs, particularly in the context of large-scale AI training. NCCL is designed specifically to optimize the collective communication patterns necessary during the training of deep learning models, where multiple GPUs need to work together harmoniously to process and share data efficiently.

NCCL leverages high-speed interconnects, such as NVLink and InfiniBand, to minimize latency and maximize throughput during the data exchange between GPUs. This is essential when training large models that may require synchronization of weights or the sharing of gradients across GPUs, as it drastically reduces the overhead caused by communication, allowing for faster training times.

While NVLink is indeed a technology that enhances the hardware-level connectivity between GPUs, it serves as a physical link rather than the communication protocol or library that manages data transfers. The DeepStream SDK and TensorRT focus on optimizing inference for AI applications rather than training, making NCCL the most appropriate choice for the specific requirement of efficient GPU communication during the training phase.