Software-driven optimisation can help tackle the hardware supply crunch

Relentless demand for AI infrastructure is raising concerns for CIOs and IT leaders around the globe. Enterprise components are becoming more expensive and harder to source, driving a fundamental shift in infrastructure economics, known as a “memory super-cycle". And it’s traditional enterprises that are the most impacted by the strain on supply.

At the same time, hyperscalers are securing years of capacity in advance and manufacturers are increasingly focused on high bandwidth memory for GPUs, the specialised chips used to power AI and data heavy workloads. As a result, costs are rising, availability is tightening and lead times are extending, leaving many enterprises struggling to respond effectively.

Historically, scaling infrastructure meant simply adding more hardware as demand increased but this is no longer an option. The industry has reached a critical juncture where buying more infrastructure isn’t a sustainable fix. Instead, organisations will need to rethink their approach, placing greater emphasis on smarter, more efficient software to navigate the ongoing hardware constraints.

The end of the hardware-first approach 

Enterprise IT has long relied on adding capacity to address performance challenges, but the current supply crunch is exposing the limitations of this approach. As demand for AI-ready infrastructure accelerates, memory costs have surged – often accounting for more than 50% of total system spend – while supply remains limited. Consequently, simply adding capacity is becoming increasingly expensive and, in many cases, unsustainable. Enterprises are being forced to rethink their infrastructure strategy and try and do more with less. This is where optimisation comes in; using software to manage resources more intelligently and efficiently. What began as a response to cost pressure now represents a broader transformation in how infrastructure is designed and operated.

In practice, this means rebalancing resource utilisation. In many environments, CPU capacity, the processing power provided by central processing units, remains underused, while workloads are constrained by memory availability. Techniques such as high-speed NVMe memory tiering, which moves less active data from expensive DRAM to cost-effective NVMe storage, allow organsations to significantly reduce memory costs and increase VM density. 

At the same time, extending the life and value of existing infrastructure has become a priority. Approaches such as intelligent oversubscription, workload balancing, and memory optimisation enable higher workload density without compromising performance. Storage efficiency also plays a key role, with data reduction techniques increasing effective capacity while unlocking stranded CPU and memory trapped in rigid configurations.

Together, these software-led strategies are reinforcing the role of private cloud platforms as a control layer for modern infrastructure, empowering enterprises with greater visibility over how resources are allocated and optimised in response to industry constraints.

Smarter scaling with software-led approaches 

The ongoing supply constraints signal a fundamental rethink in how enterprise IT is managed. The traditional approach of solving performance issues by simply adding more hardware is no longer viable. As the “memory super-cycle” reshapes the landscape, organisations are increasingly turning to software-led strategies focused on optimisation and adaptability.

By prioritising software-driven optimisation within private cloud environments, enterprises can boost efficiency, enhance agility, and scale in a more sustainable way, without having to rely on new hardware. Those that make this transition will be better equipped to handle ongoing limitations, manage costs more effectively, and maintain momentum in their digital transformation efforts.