Compare on-premises versus cloud infrastructures: Quick Reference — AI Infrastructure (NVIDIA-Certified Associate: AI Infrastructure and Operations)

AI Infrastructure: On-Premises vs Cloud Comparison

This quick reference guide provides key facts and definitions for comparing on-premises and cloud infrastructures in the context of AI Infrastructure, which is crucial for the NVIDIA-Certified Associate: AI Infrastructure and Operations exam.

1. Hardware Requirements

• On-Premises: Requires dedicated hardware, including GPUs, servers, and storage systems tailored for AI workloads.
• Cloud: Flexible hardware options provided by cloud service providers, allowing for scalable GPU resources as needed.

2. Scalability

• On-Premises: Limited by physical hardware; scaling requires purchasing and installing additional equipment.
• Cloud: Easily scalable; resources can be adjusted dynamically based on workload demands.

3. Power and Cooling Requirements

• On-Premises: Must manage power supply and cooling systems to maintain optimal operating conditions for hardware.
• Cloud: Managed by the cloud provider; users do not need to worry about physical power and cooling logistics.

4. Networking Requirements

• On-Premises: Requires setup of high-speed networking infrastructure, including switches and routers, to support data transfer.
• Cloud: Networking is managed by the provider, often featuring high-speed connections and advanced networking protocols.

5. Data Center Networking Protocols

• On-Premises: Must implement and maintain protocols such as Ethernet, InfiniBand, or RDMA.
• Cloud: Providers typically offer optimized networking protocols, reducing the burden on users.

6. High-Speed Data Center Network Options

• On-Premises: Users must invest in high-speed networking equipment to ensure performance.
• Cloud: High-speed options are often included in service packages, enhancing performance without additional costs.

7. Purpose and Benefits of a DPU

• On-Premises: DPUs can offload networking tasks from CPUs, improving performance in AI workloads.
• Cloud: DPUs are integrated into cloud services, providing similar benefits without the need for user management.

Conclusion

Choosing between on-premises and cloud infrastructures for AI workloads involves evaluating hardware requirements, scalability, power and cooling needs, networking requirements, and the benefits of advanced technologies like DPUs. This quick reference guide serves as a foundation for understanding these critical aspects in preparation for the NVIDIA-Certified Associate: AI Infrastructure and Operations exam.

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