Learning Objectives Checklist
- Indicate the key components and features of the NVIDIA data center platform
- Identify the GPU and CPU requirements for AI data centers, the different products available, and their intended use cases
- Understand the purpose and capabilities of multi-GPU systems
- Describe the multi-node GPU interconnect technology
- Determine the role of DPUs and DOCA in an AI data center
- Evaluate the benefits of using NVIDIA-Certified Systems
- Explain the basics of AI Data Center Networks
- Outline the networking requirements essential for AI data centers.
- List the main features of InfiniBand and Ethernet networking technologies employed in AI data centers.
- Describe the NVIDIA networking portfolio.
- Identify the storage requirements necessary for AI workloads
- Explain the key concepts of storage file systems and apply them in relevant scenarios
- Comprehend the benefits of using validated storage partners in an AI Data Center
- Articulate what goes into planning data center deployments and how space, power, and cooling considerations affect these plans
- Discuss how NVIDIA optimizes energy efficiency in data centers through reduced networking infrastructure combined with power-efficient GPUs
- Describe cooling architecture of GPUs in data centers
- Understand how to improve efficiency through co-location
- Explain the value of reference architectures
- Describe the information found in reference architectures
- Identify available NVIDIA reference architectures
- Describe the components in the NVIDIA BasePOD reference architecture