NCP-GENL Model Optimization Practice Question
Which hardware architecture feature is specifically leveraged by TensorRT to accelerate FP16 and INT8 matrix multiplications?
⚠ Common exam trap
Candidates often confuse Tensor Cores with CUDA cores, assuming that standard CUDA cores are the primary driver for mixed-precision acceleration rather than the specialized hardware units built for matrix math.
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
✓
NVIDIA Tensor Cores
NVIDIA Tensor Cores are specialized hardware units designed to perform mixed-precision matrix multiply-accumulate operations in a single cycle. TensorRT automatically detects the presence of these cores and maps compute-intensive layers to them. By utilizing Tensor Cores, the model achieves significantly higher throughput and reduced latency compared to using standard CUDA cores, which perform operations at a lower efficiency per clock cycle for matrix math.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
CPU AVX-512 vector instructions
Why it's wrong here
AVX-512 is a set of instruction extensions for CPUs, not NVIDIA GPUs. While CPUs can perform matrix math, they are not the primary target for TensorRT optimizations. TensorRT is designed to leverage NVIDIA's GPU architecture, specifically the specialized Tensor Cores found on modern hardware, to maximize inference performance.
- ✓
NVIDIA Tensor Cores
Why this is correct
Tensor Cores are hardware circuits designed for high-speed matrix multiplications in FP16, INT8, and other low-precision formats. TensorRT optimizes the execution graph to ensure that large matrix multiplications are dispatched to these units, providing the massive performance gains seen in modern deep learning inference workloads.
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Shared memory buffers in L1 cache
Why it's wrong here
Shared memory is an important optimization feature for inter-thread communication, but it is not a specialized matrix multiplication unit. While TensorRT makes use of shared memory to improve performance, it is the Tensor Cores that provide the specific hardware acceleration for the actual arithmetic of matrix multiplication.
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Global memory coalescing hardware
Why it's wrong here
Memory coalescing is a technique to optimize access patterns to global memory to ensure high bandwidth utilization. While important for general performance, it is a low-level memory management strategy rather than the specific compute unit designed for the high-speed matrix multiplication operations that TensorRT aims to accelerate.
About these practice questions
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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.