NCA-GENM Question 129
Select 3A company is developing a multimodal generative AI model that integrates text and image data for content creation. The team needs to design a system that can efficiently handle large datasets, perform high-speed computations, and support model training. Which combination of system components is most appropriate to meet these requirements?
- A
High-performance GPUs with Tensor Core support
- B
A large-capacity SSD for fast data storage and retrieval
- C
A CPU with single-threaded processing capabilities
- D
Distributed training frameworks like NVIDIA NeMo or PyTorch
- E
An integrated GPU without AI acceleration capabilities
Show answer and explanation
Correct answers: A, B, D
Explanation
Multimodal generative AI systems require a combination of high-performance GPUs, efficient storage solutions, and robust training frameworks to handle the intensive computational and data requirements. High-performance GPUs with Tensor Core support accelerate AI computations, SSDs improve data access speeds, and distributed training frameworks enable scalability and efficiency during model training. CPUs and integrated GPUs without AI-specific acceleration are insufficient for the requirements of such systems.
- A. Correct.
High-performance GPUs with Tensor Core support are specifically designed to accelerate AI workloads, including training and inference for multimodal models.
- B. Correct.
A large-capacity SSD ensures fast data storage and retrieval, which is critical for handling the large datasets required for multimodal AI applications.
- C. Incorrect.
While CPUs are important, single-threaded processing capabilities are not sufficient for the high-speed computations required for AI training. Multithreaded and parallel processing capabilities are preferred.
- D. Correct.
Distributed training frameworks like NVIDIA NeMo or PyTorch optimize the training process for large-scale AI models and enable efficient use of available resources.
- E. Incorrect.
An integrated GPU without AI acceleration capabilities lacks the performance required to handle the demands of multimodal generative AI workloads.