NCP-GENL GPU Acceleration and Optimization Practice Question
A developer is optimizing a generative AI model for inference on NVIDIA GPUs. They want to reduce memory footprint and improve throughput without sacrificing accuracy. Which two techniques should they apply? (Choose two.)
⚠ Common exam trap
Many candidates confuse TF32 (which accelerates compute but not memory) with true memory-reducing techniques like INT8 and sparsity.
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
✓
Use NVIDIA TensorRT with INT8 quantization and calibration.
INT8 quantization and structured sparsity both reduce memory footprint and improve throughput. INT8 quantization uses 8-bit integers, cutting memory usage by 4x compared to FP32, and Tensor Cores accelerate INT8 operations. Structured sparsity prunes weights in a 2:4 pattern, halving memory for weights and enabling sparse Tensor Core acceleration. Both are supported by TensorRT and maintain accuracy with proper calibration and fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use NVIDIA TensorRT with INT8 quantization and calibration.
Why this is correct
TensorRT with INT8 quantization reduces memory footprint and increases throughput by using 8-bit integer operations, which are faster on Tensor Cores. Calibration ensures minimal accuracy loss by determining optimal scaling factors. This is a standard optimization for inference on NVIDIA GPUs.
- ✗
Use NVIDIA GPUDirect Storage to offload model weights to NVMe.
Why it's wrong here
GPUDirect Storage accelerates data loading from storage to GPU memory, but it does not reduce the model's memory footprint during inference. Model weights must still reside in GPU memory for execution. This technique is for data pipelines, not for reducing memory usage of the model itself.
- ✗
Increase the batch size to amortize memory overhead.
Why it's wrong here
Increasing batch size typically increases memory footprint because activations for more samples are stored. It may improve throughput but at the cost of higher memory usage. It does not reduce memory footprint; in fact, it often requires more memory.
- ✗
Enable NVIDIA Ampere TF32 precision for matrix multiplications.
Why it's wrong here
TF32 is a 19-bit format that accelerates FP32 operations on Ampere GPUs, but it does not reduce memory footprint compared to FP16 or INT8. It improves compute throughput but not memory usage. For memory footprint reduction, lower precision formats like INT8 or FP16 are needed.
- ✓
Apply NVIDIA's structured sparsity with 2:4 pattern and TensorRT support.
Why this is correct
Structured sparsity with 2:4 pattern allows pruning half of the weights while maintaining accuracy, reducing memory footprint and enabling faster sparse Tensor Core operations. TensorRT supports this, providing throughput gains. It is a hardware-supported feature on Ampere and later 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.