NCA-AIIO Question 12
Select 2An organization is designing its AI infrastructure to support both training and inference workloads. Which of the following considerations are specific to inference workloads as compared to training workloads?
- A
Low latency is critical to meet real-time application requirements.
- B
Higher memory bandwidth is required to process large datasets efficiently.
- C
Power efficiency becomes a priority due to deployment at scale.
- D
The need for large-scale parallel computation to process massive datasets.
- E
Support for batch processing to optimize GPU utilization.
Show answer and explanation
Correct answers: A, C
Explanation
Inference workloads prioritize low latency to meet real-time requirements and power efficiency for cost-effective deployment at scale. In contrast, training workloads focus on high memory bandwidth, large-scale parallel computation, and batch processing to handle large datasets and optimize training efficiency.
- A. Correct.
Inference often powers real-time applications, like recommendation systems or autonomous vehicles, where low latency is critical.
- B. Incorrect.
This is more relevant to training workloads, where large datasets must be processed in parallel, requiring high memory bandwidth.
- C. Correct.
Inference, especially when deployed at scale (e.g., in edge devices or data centers), benefits from power-efficient hardware to reduce operational costs.
- D. Incorrect.
This is a consideration for training workloads, as they involve large-scale parallel computations to optimize model training.
- E. Incorrect.
Batch processing is more critical during training to maximize GPU utilization and improve throughput, not as much for real-time inference.