Free NVIDIA NCP-AIN Actual Exam Questions - Question 7 Discussion
A major cloud provider is designing a new data center to support large-scale AI workloads,
particularly for training large language models. They want to optimize their network architecture for
maximum performance and efficiency.
Why is a rail-optimized topology considered a best practice for AI network architecture in this
scenario?
C. It’s not just about GPU communication; rail-optimized topology also keeps traffic localized, which helps avoid congestion and maintains low latency as you scale up. Options A and D don’t address these key AI workload needs.
C for sure, it directly targets GPU communication which is key for AI training.
C also cuts down on bottlenecks, making GPU scaling smoother in big setups.
Probably C as well. The key is reducing latency and interference between GPUs, which is critical for heavy AI training workloads. The other options don’t really focus on GPU communication efficiency.
Maybe C, since minimizing interference and boosting GPU communication is crucial here.
C/D? D sounds off since more hops usually mean more delay, not better for AI training. C fits because rail-optimized means GPUs talk faster and cleaner with less interference.
C, it’s mainly about minimizing latency and interference in GPU communications, not about scaling or redundancy. That fits large-scale AI training where fast GPU-to-GPU data flow is key.
C, it’s about cutting interference and speeding up GPU links, not scaling.
C, because rail-optimized reduces latency in GPU communication paths.
It’s C, because gpu-to-gpu comms need to be super efficient in AI training setups.