NCA-GENL Question 173
Select 3A company wants to deploy a Large Language Model (LLM) for generating customer support responses, but they face latency issues with their current infrastructure. Which combination of system components should they prioritize to reduce latency and meet user needs?
- A
Upgrade to GPUs optimized for inferencing, such as NVIDIA Tensor Core GPUs
- B
Increase RAM capacity in the system to handle larger datasets
- C
Switch to high-speed NVMe storage for faster data access
- D
Deploy an optimized inference framework, such as NVIDIA Triton Inference Server
- E
Focus on increasing the number of CPU cores for faster parallel processing
Show answer and explanation
Correct answers: A, C, D
Explanation
To reduce latency for LLM inferencing, the company should focus on components specifically designed to handle deep learning workloads. NVIDIA Tensor Core GPUs provide the required computational power, while high-speed NVMe storage ensures faster data access. Additionally, deploying an optimized inference framework like NVIDIA Triton Inference Server helps maximize efficiency and minimize latency. Increasing RAM or CPU cores alone will not have a significant impact on LLM-specific latency issues.
- A. Correct.
GPUs optimized for inferencing, like NVIDIA Tensor Core GPUs, are designed to accelerate deep learning tasks, significantly reducing latency for LLM workloads.
- B. Incorrect.
While increasing RAM capacity can improve performance for some applications, it does not directly address latency issues for LLM inferencing, as GPUs and storage speed are more critical.
- C. Correct.
High-speed NVMe storage minimizes data transfer times between storage and compute resources, which helps reduce latency in LLM workflows.
- D. Correct.
Optimized inference frameworks, such as NVIDIA Triton Inference Server, streamline the deployment process and optimize resource utilization, leading to reduced latency in inferencing tasks.
- E. Incorrect.
Adding more CPU cores can improve certain workloads, but LLM inferencing performance is primarily driven by GPU acceleration and optimized frameworks, making this a less impactful solution.