NCA-GENL Software Development Practice Question
A developer is deploying a TensorRT-LLM optimized model on NVIDIA Triton Inference Server. They observe that the first inference request takes significantly longer than subsequent ones. Which Triton feature should they configure to reduce this initial latency?
⚠ Common exam trap
Candidates often confuse throughput optimizations like dynamic batching with latency-hiding techniques like warmup, which target different phases of the request lifecycle.
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
✓
Model warmup
Model warmup explicitly runs dummy inferences at load time, forcing the model to initialize CUDA contexts, load weights, and compile kernels before any real request arrives. This eliminates the cold-start penalty observed on the first request. Other features like dynamic batching or caching improve different aspects of performance but not initial latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Instance groups
Why it's wrong here
Instance groups define how many model instances run on available GPUs. While they affect concurrency and throughput, they do not pre-initialize the model to avoid first-call overhead. The initial latency remains because the first request still triggers lazy initialization, so this configuration alone is insufficient.
- ✗
Dynamic batching
Why it's wrong here
Dynamic batching groups multiple inference requests to improve throughput, but it does not address first-request latency. In fact, batching may add slight delay as the server waits to form a batch. It is unrelated to the cold-start overhead observed here, so it would not solve the problem.
- ✓
Model warmup
Why this is correct
Model warmup runs dummy inference requests during model loading to initialize CUDA contexts, allocate memory, and compile kernels. This moves the overhead from the first real request to the loading phase, reducing initial latency. Configuring warmup in the model's config.pbtxt is the standard solution for this scenario.
- ✗
Response cache
Why it's wrong here
Response caching stores results of previous requests to serve identical inputs faster. It does not help the very first request, which has no cached entry. Moreover, caching is only effective for repeated queries, not for reducing model initialization time. Thus it does not address the described latency.
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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 NCA-GENL 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 NCA-GENL exam.