NCA-GENL Software Development Practice Question
Which memory management strategy in TensorRT-LLM is specifically designed to minimize fragmentation and allow for efficient KV cache allocation in multi-user environments?
⚠ Common exam trap
Candidates tend to confuse generic memory optimization techniques with PagedAttention, failing to recognize how virtual memory principles apply specifically to non-contiguous KV cache allocation.
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
✓
PagedAttention.
PagedAttention is the core innovation here. It manages the Key-Value (KV) cache in fixed-size blocks, similar to how an operating system manages virtual memory. By treating the KV cache as non-contiguous memory, TensorRT-LLM can efficiently allocate and reclaim space on the fly. This prevents the memory fragmentation that usually occurs with static allocation, allowing for higher concurrency and supporting more simultaneous users without running out of GPU memory.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Static contiguous memory allocation.
Why it's wrong here
Static contiguous allocation reserves a fixed block for each sequence. Because sequences have varying lengths, this results in significant internal fragmentation and wasted memory. It is rarely used in production LLM systems because it drastically limits the number of concurrent requests that can be handled simultaneously on the GPU.
- ✓
PagedAttention.
Why this is correct
PagedAttention manages the KV cache by dividing it into small blocks, allowing for non-contiguous storage. This design eliminates the internal fragmentation associated with static allocation, enabling the server to store more sequences concurrently and effectively increasing the overall capacity of the system for handling multiple user requests.
- ✗
Global unified memory pooling.
Why it's wrong here
While unified memory is a feature of NVIDIA hardware, it refers to the abstraction between CPU and GPU memory space. It is not a strategy for managing KV cache within the LLM inference engine to prevent fragmentation; using global unified memory without block management would lead to poor performance.
- ✗
Dynamic weight quantization.
Why it's wrong here
Weight quantization reduces the memory footprint of the model parameters by using lower-precision data types. While it saves memory, it does not address the fragmentation issues inherent in managing the KV cache for different sequence lengths, which is the primary bottleneck for concurrent user throughput in inference.
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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.