NCA-GENL Question 155
Select 3You are assisting in the deployment of a large language model (LLM) under the guidance of a senior team member. During testing, the model experiences significant latency when handling high-concurrency workloads. Which of the following steps should you take to evaluate and address the scalability and performance issues?
- A
Analyze the model's throughput and latency metrics under different concurrency levels.
- B
Increase the number of GPUs allocated to the model without evaluating current resource utilization.
- C
Implement caching mechanisms to reduce redundant computations for repeated queries.
- D
Perform load testing using a variety of realistic workload scenarios to identify bottlenecks.
- E
Reduce the model size arbitrarily to improve response time.
Show answer and explanation
Correct answers: A, C, D
Explanation
To evaluate and address scalability and performance issues, it is important to rely on a systematic approach. Analyzing performance metrics, implementing optimizations like caching, and conducting realistic load testing are essential steps. These actions help identify bottlenecks and ensure that the model can meet the required performance standards without compromising quality. Arbitrary actions, such as increasing resources or reducing model size without proper analysis, may lead to inefficient or suboptimal outcomes.
- A. Correct.
Analyzing throughput and latency metrics is essential for understanding how the model performs under various levels of demand. This step helps identify specific areas where performance degrades.
- B. Incorrect.
Increasing GPU resources without evaluating current utilization might not address the root cause of the issue and could lead to inefficient resource use.
- C. Correct.
Caching mechanisms can help reduce redundant computations and improve performance, especially for repeated or similar queries.
- D. Correct.
Load testing with realistic workload scenarios is a critical step in identifying bottlenecks and ensuring the model can handle expected production loads.
- E. Incorrect.
Reducing the model size arbitrarily could degrade the model's accuracy and quality of outputs, and it may not necessarily address the scalability issue.