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Upgrading Your Data Center: Unleashing the Transformative Power of Artificial Intelligence

By Michael McNerney, Senior Vice President of Marketing and Network Security, Supermicro

Over the past year, since the launch of GPT and large language models, Artificial Intelligence (AI) has taken over the mainstream. Its rapid advancement is revolutionizing enterprises and industries across the globe, with many organizations wanting to integrate the technology into their data centers and improve their workflows. However, as easy as it sounds to incorporate AI into your organization, not many are aware of the challenges that come with integrating AI into a data center. As the demand for AI integration escalates in 2024, organizations grapple with multifaceted challenges, ranging from compatibility issues within the existing infrastructure to the integration of specialized AI hardware.

Navigating the Compatibility Landscape

One of the primary challenges in AI integration is ensuring compatibility between new AI technology and existing data center infrastructure. Legacy systems are most likely not designed to handle the computational demands of AI algorithms, leading to performance bottlenecks and compatibility issues. To overcome this hurdle, organizations need to carefully assess their existing infrastructure and make necessary upgrades to accommodate AI workloads.

A significant factor contributing to these complexities is the rapid evolution of frameworks, such as TensorFlow and PyTorch, alongside libraries tailored for AI development and optimized for specific AI-accelerated hardware. These frameworks frequently release updates that capitalize on newer hardware capabilities and optimizations. This challenge creates a potential mismatch between software needs and hardware capabilities, hindering optimal performance and potentially leading to instability and losing out to competitors.

Integrating Specialized AI Hardware

 For data centers to implement AI that meets industry standards, the infrastructure needs powerful devices that can quickly handle lots of information. Yet, many organizations still use old equipment that can't handle AI. Developing AI models for Learning and Development (L&D) requires training on massive datasets. So, to improve L&D and AI, companies need to invest in advanced technology such as AMD's Instinct MI300 Series accelerators, NVIDIA's HGX H100 GPUs, or Intel's Data Center GPU Max Series, as examples that can quickly learn on massive amounts of data. Advanced GPUs can accelerate this training process by performing many calculations simultaneously, significantly reducing the time needed to build and deploy AI solutions.

Integrating this hardware into existing data center infrastructure can be a daunting task, requiring modifications to power distribution, cooling systems, and network configurations. Careful planning and collaboration between IT and facilities teams are crucial to ensure seamless integration. Data centers may lack the necessary power and cooling capabilities for these power-hungry and heat-producing servers, necessitating costly infrastructure upgrades. Additionally, seamlessly integrating these specialized units with existing hardware and software ecosystems requires careful planning and IT expertise.

It is also worth noting that it takes a large amount of computing power and resources to run and train these models. The server computer density required by AI also creates a tremendous amount of heat. To counter this, liquid cooling innovations are taking priority across the globe to implement in many data centers wanting to integrate AI and becoming necessary with each new generation of CPUs and GPUs.

Framework Utilization

The AI landscape is characterized by a diverse array of frameworks, such as the previously mentioned ones, each offering its unique strengths and limitations. Selecting the most appropriate framework for a particular AI application is crucial for ensuring optimal performance and scalability. Organizations must carefully evaluate their specific AI requirements and consider factors such as ease of use, performance benchmarks, and community or enterprise support when making their framework selection.

Integrating the chosen framework with existing data center infrastructure and software tools can be complex, requiring careful planning and execution to avoid compatibility issues and ensure seamless integration. Organizations may need to invest in training and development to equip their IT teams with the necessary skills to effectively utilize and maintain the chosen AI framework.

Using On-Premises Solutions for Cost Reduction

Hosting your data center allows you to exercise greater control over costs, especially in the face of GPU shortages affecting cloud services. By managing infrastructure in-house, organizations can potentially reduce expenditure on cloud-based solutions and take advantage of cost efficiencies associated with optimizing their hardware for their specific AI workloads. In a recent study by Intersect360 Research, 69% of respondents agreed that using public cloud is more expensive than their on-premises systems.

While hosting a data center requires initial investments in infrastructure and specialized hardware, it presents a long-term cost-saving opportunity. Moreover, organizations gain the flexibility to tailor their hardware configurations precisely to their AI requirements, potentially minimizing compatibility issues and maximizing performance without being constrained by cloud service availability or pricing fluctuations due to GPU shortages.

Embracing the Transformative Power of AI

AI integration in data centers is not merely a technological trend; it is a transformative force that is reshaping the very core of how organizations operate. By embracing AI and overcoming the challenges associated with its integration, organizations can unlock a wealth of opportunities to enhance efficiency, gain deeper insights, and deliver exceptional value to their customers. As we navigate this transformative journey, organizations must approach AI integration with strategic foresight, collaborative effort, and a willingness to adapt.

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ABOUT THE AUTHOR

Michael McNerney serves as the Senior Vice President Marketing and Network Security of Supermicro with a proven track record of record breaking and award winning products, programs and campaigns. Michael has over two decades of experience working in the enterprise hardware industry, Prior to Supermicro, he also held leadership roles at Sun Microsystems and Hewlett-Packard.