Considerations When Purchasing and Installing GPUs for On-Premise or Data Center Deployment [Introduction Guide for Large-Scale Systems for Generative AI]

Considerations When Purchasing and Installing GPUs for On-Premise or Data Center Deployment [Introduction Guide for Large-Scale Systems for Generative AI]

With the advancement of generative AI, there are increasing demands for large-scale computing resources. One growing trend is the purchase of GPUs by companies for deployment in on-premise environments or data centers to operate large systems. This guide will outline several key considerations when introducing large-scale systems for generative AI.

1. Power Supply and Cooling Measures
Generative AI systems consume enormous amounts of power. Especially when installing multiple GPUs, attention must be paid to the following points:

• Confirming Power Supply: Ensure that the server rack can provide the necessary power, and if needed, install dedicated power circuits. Consider introducing an uninterruptible power supply (UPS) to minimize the impact of power outages. Large systems typically require 200V power, so securing adequate power is the first step.
• Enhancing the Cooling System: The heat generated by GPUs is substantial, so the cooling system in the data center or on-premise environment needs to be reinforced. Proper air conditioning and cooling system implementation are crucial for ensuring stable system operation. Given the high power consumption, the cooling system itself will also require considerable power and infrastructure.

2. Securing Physical Space

• Rack Space Check: Large systems equipped with multiple GPUs, such as an 8-GPU setup, require significant space. It’s necessary to secure rack space and ensure the racks chosen are physically sturdy enough to support the server's weight. Sizes between 6U to 8U are becoming standard.
• Future Expansion Consideration: It’s important to consider future expansion needs, not just the current requirements. If necessary, plan for additional racks or rearrange the existing space.

3. Network and Storage Configuration

• High-Speed Network Infrastructure: Large-scale AI systems require the rapid transfer of massive amounts of data. Ensuring a high-bandwidth network infrastructure with redundancy is essential to minimize the impact of network failures.
• Selecting Storage: For generative AI systems processing large datasets, fast storage capable of high read/write speeds is critical. Choose high-performance storage options, like NVMe SSDs, to enhance overall system performance.

4. Software and Middleware Management

• Compatibility with GPU Software: Ensure that GPU-targeted software, such as CUDA or ROCm, is compatible with the hardware being installed. Especially critical is managing the version compatibility between AI frameworks (like TensorFlow or PyTorch) and GPU drivers.
• Distributed Computing: In large-scale systems, distributed computing spanning multiple GPUs or servers may be necessary. Use distributed frameworks to achieve efficient computation.

5. Security and Operational Management

• Enhancing Physical Security: Physical security in the data center or on-premise environment is vital. Implement access restrictions and consider installing surveillance cameras to ensure the system’s safety.
• Operation Management: Continuously monitor the operational status of large-scale systems, including GPUs, and set up alert mechanisms for any anomalies. Remote management tools will make it easier to manage systems from a distance.

6. Utilizing Container Management Services

• Benefits of Containerization: Leveraging container technologies like Docker or Kubernetes can improve environmental reproducibility and enhance operational efficiency. In particular, it allows for efficient allocation and scaling of GPU resources.
• Implementing CI/CD: Use container management services to implement continuous integration/continuous delivery (CI/CD), allowing for quick and reliable system updates and changes.

7. Cost Management and ROI Analysis

• Initial Setup Costs: The initial investment for a large-scale system with GPUs can be substantial. In addition to the costs of servers, storage, and network devices, also account for the expense of reinforcing power and cooling systems.
• Optimizing Operational Costs: To reduce long-term operational costs, design the system to maximize power consumption and cooling efficiency. Regular maintenance and optimization are essential to prolong the system’s lifespan.

Conclusion
When purchasing and installing large-scale systems for generative AI in-house or at a data center, a broad range of considerations like those mentioned above are necessary. By carefully planning and implementing these key points, businesses can enhance their competitive edge and achieve long-term success.