HP HPE2-B08 : HPE Private Cloud AI Solutions

An enterprise is designing a solution for training a large, custom Convolutional Neural Network (CNN) for a new computer vision application. Their data science team has determined that the training process will need to be distributed across multiple GPUs to be completed in a reasonable timeframe. The training process involves intensive matrix multiplication operations.
The architect is specifying components from the HPE Private Cloud AI solution.
Which infrastructure components are critical for accelerating this specific distributed training workload?
(Select all that apply.)
```
Workload Analysis:
- AI Model: Large Convolutional Neural Network (CNN)
- Task: Distributed Training
- Key Operation: Intensive matrix multiplication
```

• A. HPE ProLiant DL325 servers for the worker nodes.
• B. NVIDIA GPUs featuring multiple Tensor Cores.
• C. The HPE Data Fabric software component.
• D. HPE GreenLake for File Storage with standard NFS over TCP/IP.
• E. An NVIDIA NVLink Bridge to connect the GPUs.

An AI development team is training a large language model that exceeds the memory capacity of a single GPU. To continue training, they need to distribute the workload across multiple GPUs within the same server.
Which combination of HPE server and NVIDIA technology is specifically designed to provide a high- bandwidth, direct communication path between GPUs, bypassing the PCIe bus for improved performance in multi-GPU training?

• A. HPE ProLiant DL325 Gen11 server with NVIDIA L4 GPUs
• B. HPE Alletra Storage MP with RoCE-capable network adapters
• C. HPE ProLiant DL380a Gen11 server with GPUDirect Storage (GDS)
• D. HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge

What is the primary purpose of using RDMA over Converged Ethernet (RoCE) in the AI interconnect fabric of an HPE Private Cloud AI solution?

• A. To allow direct memory-to-memory data transfers between nodes, bypassing the CPU kernel and reducing latency.
• B. To guarantee packet delivery over lossy internet connections using the TCP protocol.
• C. To partition a single physical network adapter into multiple virtual adapters for different tenants.
• D. To encrypt data in transit between compute nodes and storage.

A global logistics company is designing an enterprise-grade AI solution. The project has two main goals:
1. Goal 1: Develop a highly accurate, proprietary logistics optimization model by fine-tuning a foundation model on the company's massive, confidential shipping dataset (20TB). This requires a secure, high- performance, multi-node training environment.
2. Goal 2: Deploy a generative AI-powered chatbot for the customer service department. The chatbot must provide real-time shipment status and answer policy questions based on a knowledge base that is updated hourly.
The company is classified as an 'AI Pro,' with a formal AI strategy and a Center of Excellence, but they want a turnkey solution to accelerate time-to-market.
Which components and strategies should the architect propose to meet all the customer's requirements?
(Select all that apply.)
```
Customer Profile:
- Industry: Global Logistics
- AI Maturity: AI Pro
- Key Workloads: Large-scale Fine-Tuning, Real-time RAG
- Desired Solution: Turnkey, enterprise-grade private cloud
```

• A. Use HPE AI Essentials to manage the training cluster, providing features like experiment tracking and optimized resource scheduling for the fine-tuning job.
• B. Implement a Retrieval-Augmented Generation (RAG) architecture for the customer service chatbot to ensure it uses the latest, hourly-updated information.
• C. Use the HPE Private Cloud AI Large configuration with NVIDIA H100 GPUs to provide the necessary performance for the large-scale fine-tuning task.
• D. Position Al-optimized HPE ProLiant DL servers at the edge for the fine-tuning workload to reduce data transfer costs.
• E. Recommend that the customer build their own solution from individual components to have maximum control.
• F. Rely solely on public cloud services for the fine-tuning job to avoid capital expenditure on high- performance GPUs.

A financial services company, currently at the 'AI Pro' maturity level, wants to build a private cloud solution to support two primary initiatives:
1. Initiative 1: Fine-tune a proprietary 70B parameter LLM for fraud detection, requiring maximum training performance.
2. Initiative 2: Deploy a customer-facing RAG-based chatbot for 500 concurrent users.
The customer wants a single, integrated solution that can handle both workloads efficiently. They have a new data center with ample power and cooling.
Which HPE Private Cloud AI configuration should the architect recommend?

• A. Medium - Expanded, because it supports a high number of concurrent users for RAG.
• B. A custom-built solution using HPE ProLiant DL325 servers and NVIDIA L4 GPUs.
• C. Two separate solutions: a Small - Expanded for the RAG workload and a Large - Standard for the fine-tuning workload.
• D. Large - Expanded, because its NVIDIA H100 NVL GPUs and eight worker nodes are required for the intensive fine-tuning workload.