Free NVIDIA NCA-AIIO Actual Exam Questions - Question 14 Discussion
and performing complex matrix operations. The team is debating whether to use GPUs or CPUs to
achieve the best performance. What is the most compelling reason to choose GPUs over CPUs for
this specific use case?
The parallel processing strength of GPUs is a big deal here, so definitely B.
It’s B for me. The key is that GPUs handle thousands of threads at once, making them way better for matrix-heavy AI tasks than CPUs, which are more about fewer, faster cores. Options A and C focus on cache and single-thread speed, but those don’t really match the workload here. As for D, energy efficiency is nice but not the priority when you need raw performance to crunch big data fast. Even if dataset size is a concern, the question asks for the most compelling reason, and that’s definitely the parallel processing power of GPUs.
I don’t think A works here since CPUs generally have bigger caches, but that’s not the main factor in speeding up matrix operations. D sounds good for energy concerns, but startups usually prioritize performance over power use initially. C is off because AI workloads rarely rely on single-thread speed. The real edge of GPUs is their ability to run thousands of threads simultaneously, which matches well with matrix operations and large data. But yeah, it would help to know how they’re managing data movement between CPU and GPU to avoid bottlenecks. Anyone else see a different angle?
B imo. CPUs don't match GPUs on parallel tasks like matrix ops, which are core to AI workloads. The memory cache size (A) or power use (D) isn't as impactful here. Single-thread speed (C) matters less for this kind of processing.
Probably B, GPUs handle parallel tasks way better for matrix ops.