It’s no secret that artificial intelligence is driving change throughout the infrastructure stack. The dynamic, high-demand nature of AI workloads requires high-performance infrastructure that often consumes substantial energy resources, challenging sustainability goals. Investments in AI are expected to grow by 226% over the next two years, as 76% of large organizations have moved beyond limited AI adoption to more advanced use cases. In addition, AI’s energy use impacts not just organizations’ bottom lines but also the energy storage and transmission infrastructure that the public relies on. This makes it particularly critical to consider sustainability when building AI infrastructure.
Purpose-Built Infrastructure for AI and Business Goals
First, meeting the rising demands for AI performance alongside broader business objectives requires infrastructure purpose-built for each organization’s specific needs. A critical part of this is right-sizing infrastructure to match both performance workloads and strategic goals. Just as essential are efforts to consolidate workloads, standardize technologies, and centralize operations, all of which reduce complexity, enhance efficiency, and strengthen overall resilience.
Strategic Data Center Placement and Edge Computing
Second, organizations can optimize performance by strategically selecting where to place their data centers and edge computing sites, and determining which data resides where. Locating data closer to where it’s used, especially at the edge, reduces latency, improves speed, and lowers bandwidth costs. This approach also supports sustainability goals by decreasing network traffic and, in turn, reducing energy consumption. In certain industries, processing data locally at the edge enables real-time decision-making, enhances operational efficiency, and reduces energy consumption. Additionally, offloading certain data processing to the edge can ease the burden on core data centers, reducing costs and power consumption.
Flexible Consumption Models for Cost Efficiency
Third, adopting a flexible consumption model enables organizations to achieve cloud-like economics on-premises. Traditional procurement cycles are often lengthy, leading many organizations to overprovision infrastructure in anticipation of growth. While well-intentioned, this approach is frequently driven by siloed decision-making and lacks a comprehensive view of enterprise-wide needs. As a result, infrastructure is often underutilized.
Data Reduction and Availability Guarantees
Fourth, data reduction and data availability guarantees. This means optimizing how data is stored and managed, which plays a critical role in maximizing infrastructure efficiency. In addition to right-sizing infrastructure, how data is handled can significantly impact performance, costs, and resource utilization. As data volumes continue to grow exponentially, technologies like compression and deduplication become essential. These data reduction techniques can dramatically shrink storage requirements, often delivering a guaranteed 4:1 reduction ratio, meaning 400 TB of data can effectively be stored using just 100 TB of physical capacity. This reduction translates into tangible benefits: fewer hardware requirements, lower capital and operational costs, reduced physical space requirements, and lower energy consumption. All factors that contribute to a more sustainable infrastructure model. Equally important is ensuring continuous, reliable access to mission-critical applications, preventing data interruptions and keeping workloads running smoothly.
The Benefits of Software-Defined Storage
Finally, software-defined storage (SDS) delivers the agility needed to support both traditional enterprise applications and modern, cloud-native workloads. Purpose-built for hybrid cloud environments, SDS is particularly well-suited for distributed block storage scenarios that require scalability, flexibility, and high availability. By decoupling storage software from proprietary hardware, SDS enables organizations to transform standard x86 servers into powerful, SDS-enabled storage platforms. This hardware independence not only simplifies infrastructure planning but also drives significant reductions in capital and operational costs.
Aligning AI Workloads with Sustainability Goals
Meeting the growing demands of AI workloads and digital transformation may seem at odds with sustainability. But both goals are complementary. By smart architecting to business goals and performance needs to reduce overprovisioning, while using other approaches such as data reduction and smart location choices, companies will optimize performance while reducing wasted resources and energy.
