NCP-AII Question 99
Select 3You are tasked with designing storage for an AI training cluster that will handle large-scale datasets and requires high throughput for training deep learning models. Which factors should you prioritize when establishing storage requirements for the cluster?
- A
Storage bandwidth to ensure high data throughput for GPUs during training workloads
- B
Low latency access for small, random read/write operations
- C
Scalability of the storage solution to handle growing dataset sizes over time
- D
Integration with container orchestration platforms like Kubernetes
- E
Focus primarily on minimizing storage cost over performance considerations
Show answer and explanation
Correct answers: A, C, D
Explanation
AI training workloads demand high storage bandwidth to ensure GPUs are not starved of data, scalable solutions to handle growing datasets, and integration with orchestration platforms like Kubernetes for seamless management. While low latency and cost considerations are important, they are secondary to the key requirements for effective AI infrastructure design in this scenario.
- A. Correct.
Storage bandwidth is essential for AI workloads because GPUs require rapid access to large amounts of data during training. Insufficient bandwidth can create bottlenecks, reducing overall training performance.
- B. Incorrect.
While latency is important for certain types of workloads, AI training typically involves sequential reads of large datasets rather than small, random operations. This makes bandwidth a higher priority than low latency in this scenario.
- C. Correct.
As AI datasets grow over time, the storage solution must be scalable to accommodate increases in data volume. Scalability ensures the cluster can meet future storage demands.
- D. Correct.
Integration with container orchestration platforms like Kubernetes is crucial in modern AI infrastructure to enable dynamic storage provisioning and efficient resource management in containerized workloads.
- E. Incorrect.
Focusing solely on minimizing storage costs can compromise performance, especially in AI training scenarios where high throughput is needed. Cost optimization should be balanced with performance requirements.