You're reviewing a vendor spec sheet for a DGX A100 system and you see something that makes you pause — a claim that it's exclusively for CPU-based workloads. You know that doesn't smell right. Which of the following statements about the DGX A100 is actually incorrect?
Select an answer to reveal the explanation.
Short Explanation and Infographic
If the vendor tells you the DGX A100 is just for CPUs, that vendor doesn't understand the product. The A100 system was literally designed from the ground up to crush AI and data science workloads using GPUs. Everything about it — the eight A100 GPUs, the interconnects, the memory setup — screams GPU acceleration. Statement B is incorrect.
Full explanation below image
Full Explanation
The DGX A100 is NVIDIA's flagship AI system, designed specifically to accelerate deep learning, large-scale data science, and high-performance computing workloads. Eight A100 Tensor Core GPUs is the entire point. Each A100 packs 6,912 CUDA cores, 108 Tensor cores for matrix operations, and 40 GB of memory — that's 320 GB total GPU memory across the system. GPUDirect Storage lets the GPU read data directly from storage without routing through the CPU first — the CPU would just get in the way. This is essential when training models on massive datasets. NVLink is the high-speed interconnect that lets all eight GPUs communicate at 600 GB/s — crucial for distributed training. The trap is testing whether you know what the DGX A100 is designed for. It's not a general-purpose server; it's a specialized AI compute engine.