Question: 86 I Which two types of workloads benefit most from NVIDIA AI Enterprise on HPE systems?

NVIDIA AI Enterprise is an end-to-end, cloud-native suite of AI and data analytics software, optimized and certified to run on specific hardware platforms like HPE systems. Its primary purpose is to streamline the development and deployment of high-performance AI applications. The workloads that benefit most are those that are computationally intensive and can leverage the massive parallel processing capabilities of GPUs. Natural Language Processing (NLP) and Computer Vision/Deep Learning are quintessential AI workloads that rely heavily on deep neural networks, making them ideal candidates for acceleration with the NVIDIA AI Enterprise software stack and underlying HPE hardware.

C. File archiving: This is a storage-intensive, I/O-bound operation that does not require the specialized GPU acceleration or AI software frameworks provided by NVIDIA AI Enterprise.
D. Traditional batch payroll: This is a classic transactional, CPU-bound workload. It does not involve AI models and would not benefit from the parallel processing architecture of GPUs.

1. NVIDIA AI Enterprise Data Sheet (PB-10463-001v04)

Page 2

Section: "ACCELERATE EVERY AI WORKLOAD". The document states

"Key use cases include conversational AI

computer vision

recommender systems

and more." Conversational AI is a direct application of Natural Language Processing (NLP).

2. HPE Reference Configuration for NVIDIA AI Enterprise on HPE ProLiant DL380 Gen10 Plus servers

Page 5

Section: "Solution overview". The document explicitly mentions

"NVIDIA AI Enterprise... is ideal for a variety of workloads

including image classification

natural-language processing (NLP)

and speech recognition." Image classification is a core task within Computer Vision.

3. NVIDIA AI Enterprise Product Page

Section: "AI Use Cases". The official product page lists "Computer Vision" and "Conversational AI & Large Language Models (LLMs)" as primary use cases that the platform is designed to accelerate. This directly supports both correct options.