NCP-AII Question 59
Select 3You are tasked with deploying an AI workload that requires high GPU performance and scalability for training large datasets. Which server configurations would best meet these requirements while ensuring optimal performance and compatibility with NVIDIA's AI infrastructure tools?
- A
A server equipped with multiple NVIDIA A100 GPUs and NVLink interconnects.
- B
A server with only a high-performance CPU and no dedicated GPUs.
- C
A server utilizing NVIDIA DGX Systems with pre-installed AI frameworks and libraries.
- D
A server with entry-level GPUs like NVIDIA T400 for cost efficiency.
- E
A server configured with NVIDIA GPUs and CUDA-optimized software stack.
Show answer and explanation
Correct answers: A, C, E
Explanation
To deploy AI workloads effectively, the server configuration must include high-performance NVIDIA GPUs like the A100, DGX Systems for optimized pre-installed frameworks, or servers configured with CUDA-optimized stacks. These configurations ensure the necessary computational power, scalability, and software compatibility required for training large datasets. Entry-level GPUs or CPU-only configurations are insufficient for such demanding workloads.
- A. Correct.
Servers with NVIDIA A100 GPUs and NVLink interconnects provide the high GPU performance and scalability needed for AI workloads. NVLink offers high-speed GPU-to-GPU communication, which is critical for training large datasets.
- B. Incorrect.
A server with only a high-performance CPU would lack the necessary GPU acceleration for AI workloads, leading to suboptimal performance.
- C. Correct.
NVIDIA DGX Systems are specifically designed for AI workloads, with pre-installed frameworks and libraries optimized for performance, making them a suitable choice.
- D. Incorrect.
Entry-level GPUs like the NVIDIA T400 are not designed for demanding AI workloads, as they lack the performance and memory capacity required for training large datasets.
- E. Correct.
A server with NVIDIA GPUs and a CUDA-optimized software stack is essential for leveraging GPU acceleration and ensuring compatibility with NVIDIA's AI infrastructure tools.