Building AI-Ready Infrastructure: A Balanced Approach for Optimized Success
As artificial intelligence (AI) continues to drive technological advancements and help improve efficiency, cost savings and revenue potential for organizations around the world, deploying and operating successful AI systems hinges on the underlying infrastructure.
Infrastructure is a critical foundation for the AI stack – but it’s also one of the toughest challenges for IT oversight. There is no one-size-fits-all approach.
In this short blog post, I’ll break down key takeaways from IDC's latest white paper, Taking a Pragmatic Approach for AI-Ready Infrastructure, in which author Ashish Nadkarni outlines a clear, balanced approach to AI infrastructure for CIOs, enterprise architects and infrastructure & operations (I&O) teams.
AI is not comprised of a single workload or use case; it encompasses a range of tasks, from routine inferencing to complex, data-intensive model training. It has become a vital tool for many organizations across industries, driving innovation, efficiency and competitive advantage. Ways in which AI can redefine operations include:
- Enhancing operational efficiency by automating routine tasks, optimizing supply chains, and improving asset management.
- Enriching decision-making with advanced analytics and insights.
- Improving customer experience, responsiveness and accuracy with interpretive AI systems, chatbots, virtual assistants, and personalized recommendations.
- Enabling development of new products and services by leveraging data insights and advanced algorithms.
- Bolstering risk management and mitigation by analyzing patterns and anomalies in data.
- Boosting employee productivity by augmenting human capabilities, allowing employees to focus on higher-value tasks.
- Improving detection of fraud and cyberthreats using machine learning (ML) systems in use cases such as personalization and pricing optimization engines.
- Accelerating research and time to insights using generative AI systems.
This wide range of AI applications calls for varying infrastructure setups, making it essential for enterprise architecture teams to adopt a balanced approach that is customized for a specific purpose.
Infrastructure is Crucial for Successful AI Implementation
Essential ingredients for supporting AI include high-powered computing, efficient data handling, and reliable networking. But not every AI workload demands the same level of resources. Oftentimes, general-purpose processors (CPUs) can manage smaller AI workloads, while more specialized applications – like large-scale training models – require advanced accelerators (e.g. GPUs).
IDC recommends several strategies for planning and building AI-ready infrastructure:
- Assess Specific AI Requirements: Enterprise architecture teams should evaluate the specific AI use cases their business requires.
- Balance CPUs and GPUs: Create a balanced ecosystem of CPUs and GPUs designed to match the correct infrastructure with the workload.
- Prioritize Data Security and Privacy: I&O teams should consider the implementation of "private AI," running AI workloads on premises to help safeguard sensitive data.
As AI workloads continue to proliferate, IDC emphasizes the need for cost-effective infrastructure strategies. Data centers operating AI workloads consume a substantial amount of energy. Enterprise architecture teams should select energy-efficient processors, invest in cooling solutions, and implement sustainable practices to help manage operational costs. AMD EPYC™ processors are energy efficient, offering a practical solution for businesses aiming to boost performance while helping companies address sustainability goals.
A robust AI infrastructure needs visibility into compute, storage, and networking resources. I&O teams should equip data centers with observability tools to help the business understand usage patterns and help ensure the infrastructure can scale as AI demands grow.
3 Pillars for Building AI-Ready Infrastructure
IDC recommends a three-pillar framework for constructing AI infrastructure that aligns with modern demands:
- Modernize: Replace outdated servers with newer, more efficient systems to maximize space and energy savings.
- Utilize a Hybrid Cloud Strategy: For workloads that vary in intensity and scale, virtualized and containerized environments provide a flexible solution. By leveraging both private and hybrid cloud strategies, enterprises can scale AI applications while avoiding unnecessary resource allocation.
- Invest in Balanced Accelerator Resources: Organizations should right-size their investments in coprocessors (GPUs) to match specific workload needs. Pairing accelerators with capable CPUs helps ensure maximum performance without breaking the bank.
AMD EPYC Processors as a Competitive Solution
AMD EPYC processors provide a compelling option for businesses building AI-ready infrastructure. EPYC processors support a range of AI tasks, from CPU-based inferencing to hosting large-scale GPU training solutions, offering high performance with energy efficiency, aligning with many organizations' sustainability goals.
AMD emphasis on security — from silicon-level safeguards to robust data privacy features such as encryption — further strengthens its appeal.
The path to AI-readiness requires thoughtful planning and strategic investment. With insights from IDC and support from AMD EPYC processors, enterprises can embrace AI-driven innovation confidently. The right infrastructure choices will drive business results and contribute to energy-efficient, secure, and cost-effective operations. To learn more about IDC’s recommendations, check out the full white paper and discover how pragmatic AI infrastructure can transform your business.
