NCP-GENL GPU Acceleration and Optimization Practice Question
Which TWO of the following techniques are best suited for reducing the latency of LLM inference on NVIDIA GPUs?
⚠ Common exam trap
Candidates often suggest generic performance tweaks like overclocking or batch size adjustments, missing that PagedAttention and KV cache quantization are specific, high-impact techniques for LLM memory management.
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
✓
KV cache quantization.
Reducing LLM latency requires optimizing both the compute throughput and the memory access patterns. Key-Value (KV) cache quantization and PagedAttention are industry standards for LLM acceleration. KV cache quantization reduces memory bandwidth usage, while PagedAttention manages memory dynamically, preventing fragmentation. These methods significantly improve the token generation rate, which is the primary metric for user-perceived performance in large-scale generative AI deployments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
KV cache quantization.
Why this is correct
KV cache quantization compresses the storage of intermediate tokens, significantly reducing memory bandwidth consumption. Since LLM inference is often memory-bandwidth bound during the decoding phase, this technique allows more tokens to be processed concurrently and speeds up the transfer of cache data to the compute units.
- ✗
Increasing the number of CPU worker threads.
Why it's wrong here
CPU threads are not the primary drivers of LLM inference performance, which is heavily GPU-bound. Adding more CPU threads introduces overhead and context switching without addressing the core compute or memory bottlenecks found within the GPU architecture, potentially leading to increased latency rather than improved performance throughput.
- ✓
Implementing PagedAttention.
Why this is correct
PagedAttention manages memory by splitting the KV cache into non-contiguous blocks. This eliminates internal fragmentation and enables more efficient batching of requests with different sequence lengths. By improving memory efficiency, it allows for higher batch sizes and lower latency by maximizing the GPU's memory bandwidth usage.
- ✗
Disabling ECC memory on the GPU.
Why it's wrong here
Disabling ECC memory provides a negligible performance gain while significantly increasing the risk of silent data corruption in large model weights. In production LLM environments, data integrity is paramount, and the small performance overhead of ECC is an acceptable tradeoff to ensure reliability and model output accuracy.
- ✗
Switching from Tensor Cores to CUDA Cores.
Why it's wrong here
Tensor Cores are specifically architected for matrix multiplication at high speeds, which are the backbone of transformer operations. Using general-purpose CUDA cores for these operations would drastically increase computation time, leading to massive performance regressions. Tensor Cores are required for efficient deep learning workload execution on GPUs.
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 NCP-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 NCP-GENL exam.