Presented by AMD
This article is a part of a VB Special Issue titled “Fit for Purpose: Tailoring AI Infrastructure.” Discover the full collection of articles here.
Artificial Intelligence (AI) is profoundly transforming enterprise technology, driving innovations in process automation, personalized user experiences, and data insights. For organizations, integrating AI into their strategy is no longer optional; it's essential for competitive advantage. Much like Google's pivotal decision in 2016 to prioritize mobile platforms, today's organizations are declaring themselves 'AI-first,' recognizing the necessity of tailored networking and infrastructure to support AI initiatives.
Neglecting the support of AI workloads poses a significant business risk, leaving companies trailing behind more agile, AI-driven competitors that leverage technology for growth and market leadership.
While AI applications can enhance revenue and efficiency through automation, the shift can be challenging. AI workloads demand substantial processing power and storage capacity, placing immense pressure on existing enterprise infrastructures.
In addition to centralized data centers, successful AI deployments often extend to various user devices such as desktops, laptops, smartphones, and tablets. There is a growing trend of utilizing AI on edge and endpoint devices, which allows for faster data collection and analysis closer to the source. For IT teams, critical considerations revolve around infrastructure costs and placement—whether their AI solutions are optimally deployed in on-premises data centers, cloud environments, or at the edge.
How Enterprises Can Succeed with AI
To transition into an AI-first organization, developing specialized infrastructure is paramount. Most businesses lack the resources to build extensive new data centers to support energy-intensive AI applications. Instead, they must seek to modernize their existing data centers.
So, where should organizations begin? Initially, cloud service providers (CSPs) offered straightforward, scalable compute and storage solutions for general business workloads. The current landscape has evolved, with AI-centric CSPs now providing cloud solutions tailored to AI workloads and hybrid setups that integrate on-premises IT with the cloud.
Navigating the complexities of AI adoption can be daunting. Many organizations turn to strategic technology partners with expertise in AI to guide them in creating and implementing solutions that fulfill specific objectives and foster business growth.
Data centers play a vital role in AI applications, and a key function of any strategic partner is facilitating data center modernization. The rise of dedicated AI servers and processors enables organizations to deliver superior computational power using fewer components, reducing the data center footprint, enhancing energy efficiency, and lowering total cost of ownership (TCO) for AI projects.
Furthermore, a knowledgeable partner can assist in selecting the appropriate graphics processing unit (GPU) platforms, which are crucial for AI success, especially for training models and real-time processing. A properly implemented AI-specific GPU platform can optimize resources for the specific AI projects at hand, enhancing return on investment (ROI) while improving the cost-effectiveness and energy efficiency of data center resources.
It's essential to distinguish which AI workloads truly require GPU acceleration versus those that might be more efficiently handled by CPU-only infrastructure. For instance, AI inference workloads may be best suited for CPUs when model sizes are smaller or represent a minor portion of overall server workload. This consideration is vital for effective AI strategy planning, as GPU accelerators can be expensive to acquire and operate.
Equally important is data center networking, which is critical for supporting the extensive processing requirements of AI applications. Experienced technology partners can provide insights into optimal networking options across all levels, including rack, pod, and campus architectures, helping navigate the trade-offs between proprietary and industry-standard technologies.
Choosing the Right Partnerships
To transition successfully to an AI-first infrastructure, organizations should seek strategic partners that combine technical expertise with a comprehensive portfolio of AI solutions tailored for cloud environments, on-premises data centers, user devices, edge, and endpoints.
AMD is at the forefront of helping organizations harness AI in their data centers. Its EPYC™ processors facilitate rack-level consolidation, allowing enterprises to run workloads on fewer servers while enhancing CPU and GPU performance. This consolidation can free up data center space and energy, paving the way for the deployment of AI-specialized servers.
As demand for AI application support increases, aging infrastructures face significant strain. Delivering secure, reliable AI-first solutions necessitates the right technology throughout the IT landscape—from data centers to user and endpoint devices.
Enterprises should capitalize on emerging data center and server technologies to accelerate AI adoption. By aligning innovative and proven technology with strategic expertise, businesses can mitigate risks and embrace an AI-first mindset. The time to embark on this transformative journey is now.
Learn more about AMD.
Robert Hormuth is Corporate Vice President, Architecture & Strategy — Data Center Solutions Group, AMD.
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