NCA-GENL Software Development Practice Question
A developer is using TensorRT-LLM to build a chatbot and wants to reduce the memory footprint of the KV cache during inference. Which technique should they use?
⚠ Common exam trap
The trap here is assuming that increasing batch size or beam width improves efficiency, but both increase KV cache memory usage.
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
✓
Enable paged KV cache
Paged KV cache is a memory management technique in TensorRT-LLM that divides the KV cache into fixed-size blocks, allowing non-contiguous storage and dynamic allocation. This reduces fragmentation and enables more sequences to fit in memory. It is the recommended approach to minimize KV cache memory footprint during inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable quantization
Why it's wrong here
Disabling quantization would likely increase the memory footprint because quantized weights and activations use fewer bits. Quantization can also apply to the KV cache, reducing its size. Disabling it would have the opposite effect, so it is incorrect for reducing KV cache memory.
- ✗
Increase the beam width
Why it's wrong here
Increasing the beam width would require storing more KV cache entries for each beam, increasing memory usage. Beam search maintains multiple hypotheses, each with its own KV cache, so this would exacerbate the memory issue rather than reduce it. Therefore, it is incorrect.
- ✗
Use a larger batch size
Why it's wrong here
A larger batch size increases the total number of sequences processed concurrently, which increases the total KV cache memory required. While it can improve throughput, it does not reduce memory footprint per sequence and may lead to out-of-memory errors. Thus, it is not the right technique for reducing KV cache memory.
- ✓
Enable paged KV cache
Why this is correct
Paged KV cache, a feature in TensorRT-LLM, manages the KV cache in fixed-size blocks (pages) that can be allocated and freed dynamically. This reduces memory fragmentation and allows more efficient memory usage, especially for variable-length sequences. It is specifically designed to reduce KV cache memory footprint, making it the correct choice.
About these practice questions
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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.