What should companies look for in an AI data center?

This blog will help you discover the main characteristics and capabilities to look for in an AI data center, ensuring that your organization can fully leverage the power of AI.

Data center designs for supporting AI

AI workloads necessitate specialized infrastructure to support high-density deployments, efficient cooling, and robust networking. Consider the following design aspects:

High-density deployments

AI applications, particularly those involving machine learning and deep learning, rely on GPU-based deployments that demand significantly higher power per square foot. Ensure the data center can accommodate these high-density power requirements, providing higher kW per rack to maintain optimal performance. Flexential offers solutions to support high-density deployments.

Cooling concerns

The intense computational power of AI workloads generates substantial heat, necessitating advanced cooling methods. Look for data centers with liquid cooling-ready facilities and water looping systems to manage heat efficiently. These methods are more effective than traditional air cooling, ensuring equipment remains at optimal temperatures. For more insights, read about adapting data center technologies for AI.

Networking considerations

Networking within GPU clusters can become a bottleneck if not adequately addressed. Data centers should offer dense, high-speed connections between racks, utilizing technologies like InfiniBand or advanced Ethernet solutions to facilitate rapid data transfer. Getting your AI data center design right is crucial for parallel processing and cross-traffic within AI models.

Download our guide, "Data Centers and the Impact of AI," to gain valuable insights into how data centers support AI workloads. This guide covers key considerations such as differentiating between training and inference, ensuring data accessibility, and managing network latency.

High-density power requirements

To support GPU-based deployments, data centers must provide robust power infrastructure:

• Higher kW per rack: Ensure the data center can deliver higher power per square foot, as AI workloads can significantly increase power consumption. This capability is essential for maintaining the performance and reliability of AI applications.  
• Liquid cooling-ready facilities: The challenges of managing power, cooling, and infrastructure for AI workloads have become apparent. Advanced cooling techniques like liquid cooling are essential for managing the heat generated by high-density GPU deployments. Look for data centers equipped with liquid cooling-ready infrastructure to ensure efficient heat dissipation.

Hybrid and multi-cloud strategies

AI workloads often involve data stored across various environments, including public clouds, edge locations, and on-premises data centers. An AI-ready data center should support hybrid and multi-cloud strategies, providing seamless connectivity services. Flexential Network Hub facilitates end-to-end connectivity, enabling efficient data transfer to and from AI models.

Check out our infographic, Data centers in the AI era, where four industry leaders discuss the challenges and opportunities in the AI era. Discover the six emerging trends and optimize your data center strategy for the future.

Use of Data Center Infrastructure Management (DCIM)

DCIM tools play a crucial role in optimizing data center operations for AI workloads:

AI integration for DCIM

AI can be part of the solution to the challenges it creates, such as managing cooling and identifying hot spots on the data center floor. Advanced DCIM solutions go beyond identifying issues by providing actionable insights and automated solutions. Learn more about sustainable strategies for harnessing AI.

Sustainability goals

DCIM tools can help achieve sustainability goals by making power consumption more efficient. This reduces operational costs and supports corporate sustainability initiatives. Efficient energy use is critical in minimizing the environmental impact of data centers.

Operational efficiency

Another benefit of advanced DCIM tools is reducing the number of employees needed to operate a data center. This is particularly important given the industry's current shortage of skilled workers. Automation and AI-driven management solutions enable data centers to run more efficiently, with fewer personnel required on-site.

Data security

Integrating AI into DCIM enhances data security by enabling real-time monitoring and threat detection. AI algorithms can analyze vast amounts of data to identify unusual patterns and potential security breaches, providing a proactive approach to cybersecurity. Additionally, AI can automate responses to detected threats, ensuring quick and effective mitigation. Effective data security is critical in safeguarding sensitive information and maintaining trust.

Learn more about RFID temperature sensors and how they integrate into FXP to provide real-time temperature monitoring in our DCIM Environmental Sensor infographic.

Strategic considerations for AI data centers

Flexibility and scalability

Another critical factor is the ability to scale infrastructure to meet growing AI demands. AI workloads can be unpredictable, requiring data centers to be flexible and scalable. Look for data centers that offer modular designs and scalable power and cooling solutions. This flexibility ensures that as your AI initiatives expand, your data center can grow with them, providing continued support without significant overhauls.

Compliance and regulations

Compliance with industry regulations and standards is crucial, especially in healthcare, finance, and government sectors. Data centers must adhere to strict security protocols and compliance requirements to protect sensitive data and ensure privacy. Ensure that the AI-ready data center you choose complies with relevant regulations and can provide documentation and support for compliance audits.

Making the right choice for your AI data center

Choosing the right AI data center involves evaluating multiple factors, from high-density power requirements and advanced cooling methods to robust networking and DCIM tools. By considering these elements, you can ensure your data center is equipped to handle the demands of AI workloads, providing the performance, efficiency, and scalability needed for successful AI deployment.

Learn more about Flexential data center design, and explore how Flexential supports high-density deployments.