NCP-AIO Troubleshooting and Optimization Practice Question
A production inference service running on NVIDIA T4 GPUs shows that GPU utilization is consistently below 20% while request latency is high. Profiling with Nsight Systems reveals that the model execution time is short but there are frequent gaps between kernels. Which optimization should be applied first to improve GPU utilization?
⚠ Common exam trap
The trap here is focusing on kernel launch overhead as the primary cause, when the real issue is insufficient work per launch due to small batch sizes.
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
✓
Increase the batch size in the inference server configuration.
The low GPU utilization and gaps between kernels indicate that the GPU is not receiving enough work per launch. Increasing the batch size allows more data to be processed per kernel, filling the gaps and raising utilization. This is the most direct and effective first step before considering more complex techniques like CUDA graphs or precision changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the batch size in the inference server configuration.
Why this is correct
Increasing batch size allows more requests to be processed per kernel launch, reducing the relative overhead of kernel launch gaps and improving GPU utilization. With small batches, the GPU sits idle between kernels. Larger batches keep the GPU busy and amortize launch overhead, directly addressing the observed gaps and low utilization. This is a standard first optimization for underutilized inference GPUs.
- ✗
Switch from FP32 to FP16 precision for the model weights.
Why it's wrong here
Using FP16 reduces memory bandwidth and can speed up computation, but it does not directly address the gaps between kernels. If the GPU is idle due to small batches, precision changes will not fill those gaps. The utilization problem is about scheduling and batch size, not arithmetic throughput. FP16 is beneficial but secondary here.
- ✗
Increase the number of concurrent model instances on each GPU.
Why it's wrong here
Running multiple model instances can improve utilization by interleaving work, but it increases memory consumption and may not be feasible on T4 GPUs with limited memory. It also adds complexity and can cause contention. The simpler and more direct fix for low utilization due to small batches is to increase batch size. Multiple instances are a later optimization.
- ✗
Enable CUDA graphs to capture and replay the inference sequence.
Why it's wrong here
CUDA graphs reduce launch overhead by capturing a sequence of kernels and replaying them with a single launch. While this can help with gaps, it is more complex to implement and may not address the root cause if the gaps are due to small batch sizes. The primary issue is insufficient work per launch, not launch overhead itself. Batching should be addressed first.
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JA
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
This NCP-AIO practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-AIO exam.