NCP-GENL Production Monitoring and Reliability Practice Question
A production LLM service on NVIDIA Triton Inference Server is deployed across multiple GPUs. The team notices that one GPU consistently shows higher latency for inference requests compared to others, despite similar utilization. Which NVIDIA tool should be used to investigate per-GPU performance discrepancies and identify bottlenecks?
⚠ Common exam trap
The trap here is relying on monitoring tools like DCGM or nvidia-smi that provide metrics but not the granular profiling needed to identify kernel-level bottlenecks on a specific GPU.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
NVIDIA Nsight Systems
Nsight Systems provides detailed profiling of GPU activities, including kernel execution, memory transfers, and synchronization. By capturing a timeline across all GPUs, it can reveal why one GPU lags, such as longer kernel runtimes or thermal issues. This level of detail is essential for diagnosing per-GPU performance discrepancies and optimizing LLM inference in a multi-GPU deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
NVIDIA Nsight Systems
Why this is correct
Nsight Systems is a system-wide profiling tool that can capture GPU activities, including kernel execution times and memory transfers, across multiple GPUs. It can highlight per-GPU performance differences by showing timelines and bottlenecks. In this scenario, it can identify why one GPU is slower, such as inefficient kernel usage or thermal throttling, enabling targeted optimization.
- ✗
NVIDIA Triton Inference Server metrics
Why it's wrong here
Triton metrics report inference counts, latencies, and queue times per model, but they do not break down performance by GPU. They can show that latency is higher for a model, but not which GPU is responsible or why. To attribute latency to a specific GPU and diagnose bottlenecks, a lower-level profiling tool is needed.
- ✗
NVIDIA Data Center GPU Manager (DCGM)
Why it's wrong here
DCGM provides aggregate and per-GPU metrics like utilization, memory usage, and temperature, but it does not offer detailed kernel-level profiling. While it can indicate that one GPU is slower, it cannot pinpoint the exact cause such as specific kernel inefficiencies. For deep performance analysis, a profiler like Nsight Systems is required.
- ✗
NVIDIA System Management Interface (nvidia-smi)
Why it's wrong here
nvidia-smi provides basic per-GPU metrics like utilization and memory, but it lacks the granularity to diagnose performance discrepancies. It can show that one GPU has higher utilization, but not the underlying reasons such as kernel execution times. It is not a profiling tool and cannot identify bottlenecks within inference execution.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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