Legacy Compute to AI: How Enterprises Are Navigating the AI Infrastructure Market

Enterprise AI has entered a new phase.

The early focus on AI experimentation and proof-of-concept has given way to tangible production realities: performance requirements, cost control, security, governance, and long-term infrastructure planning.

For CIOs and CTOs, AI is no longer an isolated initiative, it must coexist with, and become embedded in established enterprise IT environments.

As a result, the conversation around AI infrastructure is changing. It is moving away from short-term scarcity and specification-driven decisions, towards availability, lifecycle management, and sustainable value creation.

AI Infrastructure Must Evolve, Not Replace

Despite frequent predictions of its decline, traditional IT remains central to enterprise operations. CPU-based systems continue to underpin core applications, data platforms, and business-critical services. AI infrastructure does not replace this foundation; it extends it.

For most organisations, success depends on integrating AI compute into existing data centre environments, without destabilising proven systems or introducing unnecessary risk. This requires careful alignment between traditional infrastructure and GPU-accelerated platforms, rather than a wholesale re-architecture of the data centre.

The challenge for enterprise leaders is therefore not whether to adopt AI infrastructure, but how to do so responsibly and pragmatically.

The NVIDIA Hopper Generation in an Enterprise Context

The NVIDIA Hopper generation including H100 and H200 platforms, has become foundational to enterprise AI adoption. These GPUs are widely adopted by organisations across training, inference, and high-performance computing workloads, delivered through GPU-dense systems such as DGX platforms.

While newer architectures continue to emerge, many enterprises remain focused on Hopper-class infrastructure because it strikes a balance between capability, ecosystem maturity, and operational readiness. H100-based systems have proven themselves in production environments where reliability and supportability matter as much as raw performance.

At the same time, the market is beginning to normalise.

As next-generation deployments increase, including platforms built on newer architectures, Hopper and Ampere GPUs are increasingly appearing in secondary and refurbished channels. This pattern is consistent with long-established enterprise IT lifecycles, where infrastructure moves through phases of primary deployment, reuse, and responsible retirement.

Market Maturity: Availability, Pricing, and Enterprise Demand

What is notable in the current market is not simply increased availability, but sustained enterprise demand.

While supply in secondary and refurbished channels is improving, pricing for enterprise-grade GPU platforms has remained broadly stable. This reflects ongoing demand from organisations seeking proven AI compute without the cost, lead times, or risk associated with bleeding-edge deployments.

For many enterprises, this represents an opportunity:

• Access to high-performance AI infrastructure
• Greater flexibility in procurement models
• Improved alignment between investment and actual workload requirements

AI infrastructure is beginning to behave more like traditional enterprise IT, where value is extracted across the full lifecycle, rather than concentrated solely at first deployment.

GPU-Dense Platforms and the Reality of Lifecycle Management

AI platforms built around GPU-dense architectures introduce complexities that extend well beyond initial deployment. Systems such as DGX servers concentrate significant compute, high-bandwidth memory, and interconnect capability into compact footprints, making them among the most powerful, and valuable, assets within the enterprise IT estate.

Managing these systems responsibly requires specialist knowledge across their full lifecycle. Unlike traditional server infrastructure, GPU-dense AI platforms demand careful handling at decommissioning to address security, compliance, and residual value considerations associated with tightly integrated compute and memory components.

RTK has supported enterprise environments involving the decommissioning of AI infrastructure built on GPU-dense platforms, including DGX-based systems. These engagements reinforce a critical reality for enterprise leaders: AI hardware is specialist infrastructure, and its retirement must be managed with the same discipline and expertise applied to any other mission-critical system.

High Bandwidth Memory (HBM): Performance Enabler and Risk Consideration

High Bandwidth Memory (HBM) is central to the performance of modern AI accelerators, enabling the data throughput required for large-scale AI workloads. It is also one of the most sensitive components in the AI hardware stack.

From an enterprise perspective, HBM introduces considerations around:

• Data security
• Compliance
• Residual value
• Responsible reuse or retirement

RTK has direct experience handling and decommissioning AI hardware incorporating HBM, applying secure and compliant processes appropriate for enterprise environments. While often overlooked in strategic discussions, memory handling is a critical part of AI infrastructure governance, particularly as platforms move into secondary and refurbished use.

Procurement, Refurbishment, and Cloud: Expanding the AI Compute Toolkit

As the AI infrastructure market matures, enterprises are increasingly adopting diversified strategies.

Some workloads remain best suited to on-premise AI infrastructure, where control, predictability, and data governance are paramount. Others benefit from cloud-based AI compute, particularly where access to the newest architectures or elastic capacity is required.

RTK supports organisations across this spectrum from sourcing enterprise-grade AI hardware, including Hopper-class GPUs, to accelerating cloud journeys where raw compute or rapid scalability is the priority. This flexibility allows organisations to align infrastructure decisions with business objectives, rather than forcing all workloads into a single model.

In practice, we are increasingly seeing organisations reassess how and where AI workloads are best executed. In one recent engagement, an enterprise chose to decommission on-premise GPU infrastructure and transition specific AI workloads to the cloud in order to access newer architectures at lower short-term cost. This allowed the organisation to maintain AI capability while deferring capital investment and avoiding premature hardware refresh cycles.

A Lifecycle Perspective on AI Infrastructure

The defining characteristic of mature AI adoption is not hardware choice alone, but lifecycle discipline.

Enterprises that treat AI infrastructure as a lifecycle, encompassing procurement, integration, operation, reuse, and decommissioning are better positioned to manage cost, reduce risk, and meet sustainability objectives. As GPU platforms transition into secondary markets, this lifecycle perspective becomes even more important.

RTK works within the data centre, focusing on the IT layer, to help organisations manage this evolution responsibly and effectively.

On-Premises AI: Looking Ahead

The presence of newer GPU architectures does not diminish the value of H100-class infrastructure. Instead, it signals a transition to a more balanced and accessible AI compute market, one where enterprises can make informed decisions based on availability, suitability, and long-term value.

For many organisations, success will not come from chasing the newest platform, but from deploying AI infrastructure that fits their environment today while preserving flexibility for tomorrow.

Enterprise AI Infrastructure with RTK

AI infrastructure leadership is increasingly defined by judgement rather than novelty. As the market matures, enterprises that understand how to access, integrate, and manage AI compute across its full lifecycle will be best placed to turn capability into lasting value.

RTK supports enterprises with both the acquisition and retirement of high-performance AI infrastructure, including access to Hopper- and Ampere-class GPU platforms, and the secure decommissioning of GPU-dense systems, enabling organisations to evolve AI capability without unnecessary risk or disruption.

Ready to explore your on-premises AI infrastructure options? Start a scoping session here.