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Generative AI’s Revolutionary Impact in Data Centers

The data center industry is seeing a transformative era marked by the rise of Generative Artificial Intelligence (GenAI). This technology, which includes advanced models like GPT-4, is reshaping the landscape of data processing and management. For CEOs and CIOs, understanding the implications of this shift and preparing existing data center resources for the GenAI wave is crucial.

Generative AI (GenAI): Defining Models, Training, and Inference

An AI model, particularly in the context of Generative AI, is akin to a virtual brain. It’s a sophisticated program that applies algorithms to data for recognizing patterns, making predictions, or decisions. Training an AI model is similar to educating a human brain. It involves providing the model with a large amount of data so it can learn and understand patterns. The more data you provide to the model, the “smarter” the model will become. This means that an AI model will, like a human brain, attempt to think through situations, adapting to them over time and improving its decision-making capabilities. Training a model enables the AI to perform tasks like identifying objects in images or understanding human language.

What is GenAI Inference?

Inference is applying a trained AI model to new, unseen data. The AI model can infer an answer based on its training. Inference is putting the model’s training into production, enabling AI to make predictions or decisions about the brand-new information it encounters. For instance, a model trained to recognize customer sentiment can analyze new customer feedback to determine whether it’s positive or negative.

Training GenAI and Getting to Inference

So, finding a model, gathering data, and training the model for inference are the foundational tasks any IT leader has to complete before rolling out GenAI to the company. However, businesses, especially in highly regulated industries like finance and healthcare, must consider the infrastructure implications of deploying these solutions. On-premises data centers are likely to be their go-to solution for taking trained AI models into productive capacity. This approach is preferred over public cloud solutions due to the sensitive nature of data and stringent regulatory requirements. It ensures better control, security, and compliance with industry standards.

Because innovation is the fuel for competition, let’s discuss what an organization with a private data center needs to successfully implement GenAI to supercharge their business to compete and win in the market.

The Current State of GenAI in Private Data Centers

In my decades of experience in the tech industry, I’ve seen many changes in business IT, some evolutionary, some revolutionary, but few are as potentially transformative as the current surge in GenAI. It’s considered an overused term, but there really is a paradigm shift coming. At the core of this fundamental change in how data centers operate and manage the burgeoning demands of AI-driven processes are three things:

  1. Increased Demand and Capacity Challenges: The introduction of publicly accessible GenAI models has led to a surge in AI demand, resulting in a frenzy of data center leasing. There is a growing need to host large quantities of AI-specialized servers, secure storage, and increasingly expensive electricity in support of moving from AI modeling to AI inference. This demand is not only driving the need for more capacity but also posing challenges for data center operators already grappling with existing demands.
  2. Infrastructure and Cost Implications: According to Tirias Research, as cited by Forbes, the data center server infrastructure and operating costs for GenAI are projected to exceed $76 billion by 2028. This cost escalation is driven by the need for more sophisticated hardware to handle the increasing complexity and workload of GenAI applications.
  3. Innovations in AI Chips: Competition breeds innovation, and chipmaker Nvidia supplies the GenAI-centric chipsets to server manufacturers such as HPE, Dell, and Lenovo. AMD just launched its Instinct MI300 Series of competitive GenAI accelerators in response. Even top cloud services providers like Microsoft and Amazon are developing their own custom AI chips to better cater to the needs of GenAI. Powerful custom-built processors such as Microsoft’s Azure Maia AI Accelerator and Amazon’s Trainium and Inferentia are examples of this trend, highlighting the industry’s move towards specialized hardware for AI tasks.

GenAI in 2024: A Guide for Businesses

Riding the fast-approaching wave of GenAI change is not just about technological upgrades; it’s about strategic foresight and adaptability. Businesses with data center resources must brace for a significant transformation. The preparation for which involves more than just some hardware upgrades. The impending GenAI era demands a rethinking of traditional data center models and a proactive stance towards the evolving landscape.

5 GenAI Focus Areas for IT Leaders in 2024

There are five focus areas that IT leaders and business decision-makers alike must prioritize to ensure they are well prepared for GenAI to drive innovation and growth in their businesses:

  1. Modernize Infrastructure: While basic data modeling for inference AI is manageable on most hyperscale cloud platforms, the leap to full-scale GenAI production is a different ball game. Generative AI for organizations with private data center resources demands a significant overhaul in terms of powerful AI-specific chipsets and enhanced data protection measures in those data centers. The rush to acquire the specialized hardware for GenAI in the next few years will likely create supply chain bottlenecks, so working with a partner who can design, procure, and implement GenAI in your data center is a strategic move.
  2. Emphasize Data Security: While everyone recognizes that data quality is crucial for training GenAI models for accuracy, the fact is that data security is equally important. For organizations using a private data center, manage your data effectively while ensuring privacy and security. This includes data collection, cleaning, and preprocessing for training AI models. Ensure compliance with data protection regulations Analytics are extremely important when it comes to security but also for the maintenance and monitoring of your GenAI implementation. Continuously monitor the performance of your AI systems. This includes regular maintenance of hardware, updating AI models, and ensuring the systems are running efficiently.
  3. Focus on Collaboration: Success in the GenAI landscape will depend heavily on collaborative efforts within the supplier ecosystem. Few businesses will have the in-house expertise to architect and deploy the specialized server farms built on AMD MI300 AI accelerated processing units (APUs) that deliver computational speed for advanced discovery, modeling and prediction while providing breakthrough performance, density, and power efficiency. Companies will likely need expert guidance on setting up the vast amount of secure storage needed to support GenAI on-prem, so collaborating with an experienced data center modernization partner will undoubtedly pay dividends.
  4. Plan to Control Costs: With the projected rise in operating costs, businesses must strategically plan their investments. CEOs and CIOs should work with CFOs on either budgeting for the higher costs of running GenAI applications or exploring ways to offset these expenses.
  5. Stay Informed: The GenAI field is rapidly evolving. Staying informed about the latest developments can help, but it is crucial for a business and its leaders to be flexible enough in their processes so as to quickly adapt to—and ultimately adopt—new technologies.

How do you accelerate your AI transition?

The integration of GenAI in data centers is not just a technological upgrade but a strategic business decision. In 2024, CEOs and CIOs must navigate this new landscape with a focus on how they can intelligently modernize their IT infrastructure without sacrificing security and in a way that controls costs. Blue Mantis is uniquely positioned to help businesses meet the rising demands of GenAI but also leverage its potential to drive business growth and efficiency.

If your business has private or colocated data centers, our Data Center Modernization solutions provide a security-first infrastructure designed, deployed, and supported by Blue Mantis. Your GenAI-ready solution will benefit from the collaboration of our world-class Cybersecurity, Cloud, Network, Managed Services, and other practice experts. Blue Mantis partners with all the major data center hardware providers to ensure your modernization solution is future-proof and energy-efficient. We can also optimize for cost by integrating the services of our FinOps team, which has been proven to reduce our customers’ annual cloud costs by 30% or more.

Take Advantage of GenAI in Your Business in 2024 and Beyond.

Contact us to discuss how we can provide you with an actionable strategic plan for modernizing your data center for Generative AI.

Tim Ferris.

Tim Ferris

Practice Manager – DC Modernization

Tim has over 20 years of combined technical, managerial, engineering, and analytical expertise.  After serving as IT Manager for several years at GreenPages, he moved into a delivery consulting role where he was a key contributor to our VMware delivery team, recognized by VMware as one of the leading delivery partners in the country.  Tim has extensive VMware and virtualization experience on large-scale projects for enterprise clients. He is also a subject matter expert in Data Protection and Storage with extensive experience designing and supporting large, complex, backup and disaster recovery environments.  ​

With a solid foundation in legacy datacenter technologies, Tim has expanded his skillset to include Cloud and Hybrid-Cloud architectures.  Tim holds numerous Azure, AWS and other industry certifications and is constantly learning in an effort to stay ahead of our rapidly evolving industry.