NCA-GENL Question 7
Select 4You are assisting in the deployment of a large language model (LLM) for a client application under the supervision of senior engineers. During testing, the model shows slower response times as the number of concurrent users increases. Which actions should you recommend to evaluate and improve the model's scalability and performance?
- A
Monitor GPU utilization and memory usage during peak load periods to identify bottlenecks.
- B
Scale the model vertically by upgrading to a more powerful GPU instance without further analysis.
- C
Simulate different levels of user concurrency using load testing tools to assess performance under various conditions.
- D
Optimize the model by reducing its size, such as using quantization or pruning techniques, while maintaining acceptable accuracy.
- E
Deploy the model on multiple GPUs or nodes and implement load balancing to distribute the workload.
Show answer and explanation
Correct answers: A, C, D, E
Explanation
Improving scalability and performance of LLMs requires a combination of monitoring system metrics, testing under various conditions, and applying optimization techniques. Monitoring utilization identifies bottlenecks, load testing evaluates system behavior under different scenarios, and techniques like model optimization and distributed deployment address performance bottlenecks effectively. Scaling vertically without further analysis is not a sustainable approach for long-term scalability.
- A. Correct.
Monitoring GPU utilization and memory usage provides insights into hardware bottlenecks, which is crucial for diagnosing performance issues.
- B. Incorrect.
Scaling the model vertically without analysis may temporarily improve performance, but it does not address underlying scalability issues or evaluate the system thoroughly.
- C. Correct.
Load testing with simulated concurrency is a standard approach to evaluate how the model performs under different workloads.
- D. Correct.
Optimizing the model by reducing its size can improve performance and scalability while preserving accuracy, which is a recommended approach.
- E. Correct.
Deploying the model on multiple GPUs or nodes and using load balancing improves scalability and distributes the workload, addressing performance issues for concurrent users.