Overcoming the AI Hardware Shortage in Data Centers

“Shortage” is not a word the IT industry is used to hearing. Cloud platforms “promise” limitless capacity. Hardware providers have built brands based on the notion that there is an endless supply of whatever chip or device you could ever wish for. It may come as something of a surprise to learn the industry is facing a fairly pronounced hardware shortage, one that will last for at least another year.

The explosion in data center construction and related hardware procurement, driven primarily by the growing adoption of artificial intelligence (AI), has put relentless pressure on hardware manufacturers. They can’t keep up with demand. Take memory. As of today, data centers are on track to consume roughly 70% of global memory output in 2026. That’s up from 20-30% in 2022.

If you need to buy memory, CPUs, or GPUs, you will face higher prices or a lack of inventory. This article examines the AI data center memory shortage and related problems, such as the GPU shortage, describing why this issue has arisen — and what you can do about it.

How is AI Causing Shortages for Data Centers?

AI’s growth has led to a boom in data center construction. According to data from McKinsey, AI accounts for approximately 70% of new colocation data center capacity. AI is further projected to drive 50% to 70% of all data center capacity by 2030.

This boom has created a parallel increase in demand for specialized hardware for AI workloads. For example, AI requires high-bandwidth memory (HBM or “HBM memory”) due to the massive parallel calculations inherent in running large language models (LLMs) and neural networks. Manufacturing capacity for HBM and double data rate 5 (DDR5) memory is finite, so until more manufacturing facilities come online, there will be shortages of these memory chips.

What’s happening in the bigger picture is best understood as an unfortunate chain reaction arising from a zero-sum game. AI demand led memory makers to allocate their manufacturing capacity to HBM and DDR5, which earn them high margins. At the same time, a packaging bottleneck has emerged with chip-on-wafer-on-substrate (CoWoS), which combines GPUs and HBM on a single piece of silicon. This design results in functionality and performance that are ideal for AI workloads, but it uses so much silicon that other silicon-dependent products experience manufacturing delays. HBM, which is also silicon-intensive, has a similar impact on the manufacturing of other hardware components.

Hyperscalers like Google and Amazon Web Services (AWS), in turn, have locked up multi-year supply contracts for this hardware, leaving everyone else to compete for what’s left. Prices have shot up, and in many cases, the hardware is simply not available.

The current situation stands in contrast with chip shortages during the pandemic. In that case, the problem was related to logistics, and it corrected itself as shipping came back from lockdown. This is different. Now, the industry has bet long-term on wafer capacity and related manufacturing.

The State of the Data Center Shortage in 2026

Discussions of the “AI chip shortage” sometimes miss the fact that there are multiple shortages occurring at the same time.The situation today includes shortages in memory and graphical processing units (GPUs).And, while there is not a shortage of conventional central processing units (CPUs), the limited supply of DDR5 memory, which is necessary for servers, has affected the availability of servers.

The last point is significant because it shows how the AI trend is having an impact on hardware for non-AI workloads. Businesses that need servers to run optimally, to simply run a website, are facing high costs for RAM, a global ram shortage, and stock-outs of servers as a result of AI.

Memory Shortage: The DRAM and NAND Squeeze

Just three companies — Samsung, SK Hynix, and Micron — manufacture between 90% and 95% of the world’s dynamic random-access memory (DRAM) and non-volatile “Not-And” (NAND) memory, which makes up the core memory of all PCs, smart devices, and servers.

When these manufacturers shifted their capacity to HBM and DDR5, this caused the AI data center NAND DRAM shortage, which has, in turn, led to much higher prices. Recently, the contract price for DRAM has leapt by 58%, and Gartner has forecast that DRAM prices could increase by 125% in 2026.

High-Bandwidth Memory (HBM): The Leading Bottleneck

The HBM shortage is problematic for enterprises that need HBM for their infrastructure. However, the problem is more complex and consequential than just a shortage of HBM.

Manufacturers have increased their HBM production. The problem is that HBM uses up to 4x the silicon wafer capacity of standard DDR5 system RAM. As a result, every wafer that goes to make an HBM stack reduces the amount of server- or consumer-grade memory that can be produced. H3 – Data Center GPU Supply Shortage: Sold Out and Staying That Way

The IT sector is experiencing a GPU shortage. However, the shortage is only partly a matter of limited GPU fabrication and supply. Rather, several upstream market forces are making the GPU shortage worse.

One theory is that hyperscalers are tactically reserving billions of dollars’ worth of GPU production in advance. Other buyers are being crowded out of the market.

Another data center GPU supply shortage problem has to do with a supply chain bottleneck for HBM. Consider the following illustrative example: The NVIDIA H100 SXM5 GPU uses HBM3, which is in short supply. Without enough HBM3, it’s impossible for NVIDIA to keep up with demand for SXM5s, so an NVIDIA GPU shortage occurs. On a comparable front, Taiwan Semiconductor Manufacturing Company’s (TSMC’s) CoWoS packaging process has to bond HBM dies onto its GPU substrate.

Unfortunately, the company has fully allocated its CoWoS capacity at least through the middle of 2027. While Samsung and Micron are responding by increasing their production of HBM, this will not cure the shortage in 2026.

The CPU Shortage: The Impact on Servers

A similar supply chain bottleneck is affecting the supply of servers. Some call this the “CPU shortage,” but this is not accurate. There are plenty of CPUs available for sale. However, with a shortage of DDR5 server memory and delays in delivery, the practical impacts include delays in delivery of servers — and higher prices.

What Problems are IT Teams Facing Due to Memory Shortages?

The memory shortages and related issues, like delays in server production, are causing a number of problems for IT teams. Budgets are taking a hit, for one thing, with high prices affecting what IT departments can afford. The difficulties run deeper, however, as explained below.

1. Lead Times and Project Delays

The lead times for large DRAM orders have ballooned from 8 to 16 weeks last year to over 40 weeks today.

Waiting nearly a year for DRAM means that projects like server refreshes or increased infrastructure scale are being put on hold. For companies that need increased scale due to growth or customer experience, these delays will have a negative impact on business outcomes.

2. The Slowing Down of Innovation

The price increases and delays are coming at a bad time. Many IT organizations are under pressure to stand up infrastructure to support AI initiatives and digital transformation projects.

IT, often criticized for being unresponsive to business requests, must now consider even slower rollouts of needed technology.

3. Loss of Agility

Delays and unavailability of essential hardware have an impact on IT infrastructure automation and agility. A company may want to pursue an “AI-first” strategy, where some repetitive processes are managed by AI, but if the IT department can’t source the infrastructure components it needs to support those strategies, it will be at a standstill.

Even something as simple as deploying a new class of server to support an agile business strategy will have to be put on hold.

4. Architectural and Design Consequences

A lack of DRAM limits architectural and design choices. The shortage may force IT to rely on more conservative, standardized server configurations. Infrastructure managers will have to make do with DDR4, for instance, because it is not possible to upgrade memory at this time.

When will the GPU and Memory Shortage End?

Though it may feel that way, this situation will not last forever. Industry analysts forecast that the AI-driven buying will wind down as more HBM and DRAM production capacity comes online.

According to Micron (in their Q3 2026 earnings report), this is unlikely to occur before the end of 2027, with a potential improvement in 2028. Some of the new fabrication facilities will be for HBM only, so its production will not alleviate the shortage of conventional DRAM. Eighteen months is an eternity for IT organizations that face pressure to refresh servers and operationalize digital strategies.

For more information on when the shortage may end, watch the below clip from a recent webinar from Park Place Technologies. Here, Rob Brothers from IDC shares his views:

6 Solutions to Overcome AI’s Impact on Data Centers

IT departments do have options when it comes to dealing with the memory and chip shortages caused by the growth of AI data centers. Some involve making the most of what is already on the racks. Others rely on selective outsourcing.

1. Migrate Select Data and Workloads to the Cloud

If the shortages are limiting your ability to deploy new hardware in your facility, the smart move might be to migrate data center processes and workloads to facilities that don’t have this problem.

When considering cloud migration planning, it’s vital to evaluate cloud providers carefully. Many hyperscalers and co-locations have limited space and availability due to AI expansion and growth in enterprise use of the Cloud. Success may come from the many leading private infrastructure as a service (IaaS) providers, where companies offer private IaaS that enables workload migration to state-of-the-art AI-enabled technology.

2. Extend the Life of Existing Hardware

If you can’t replace what you have, you should extend the life of those existing hardware assets. This might mean utilizing data center hardware maintenance to keep in-spec and end-of-service-life (EOSL) equipment running past original equipment manufacturer (OEM) date.

By taking this approach, you can defer a forced refresh in the middle of this period of high prices and hardware shortages.

3. Pre-Owned or Refurbished Hardware

It may also be viable to bypass OEM lead times and the premium being charged for DDR5. The approach involves relying on DDR4-native server platforms, e.g., earlier Xeon/EPYC generations that sit largely outside the current pricing dynamics.

Pre-owned IT hardware is typically available immediately versus the long fulfillment delays on new gear.

4. Optimize Your Current Environment

Optimizing your current environment is another way to get more use out of existing hardware before needing to replace it. The optimization can take several forms. At a minimum, it’s wise to monitor the IT infrastructure and continuously seek ways to optimize capacity. Workload tiering can help, while predictive failure monitoring makes it possible to avoid adding too much redundant capacity. Remote managed services can be a big help in implementing these processes.

It also makes sense to focus on software. Indeed, software optimization can lead to gains in hardware utilization, which further reduces the need for upgrades. Specifics include mixed-precision computing, adding memory-efficient attention mechanisms, and optimized compilers. On a related front, it is possible to increase the efficiency of existing DRAMs, such as through memory compression and in-memory caching.

5. Upgrade Selectively

Selective upgrading provides a way to avoid the worst impacts of the shortages and price spikes. For example, rather than replacing entire clusters, it may be wise to increase DRAM in bottleneck servers.

This will fix performance issues without requiring a big order of DRAM. Or, upgrade networking before compute if data movement is the limiting factor.

6. Change Procurement and Supply Chain Practices

It’s important to remember that just because your regular hardware vendor is out of DRAMs, not all hardware vendors are similarly afflicted. While it can be difficult to shift to different suppliers, given corporate procurement policies and procedures, adding vendors could be a very good move at this time.

The order cycle can be part of the picture, too. If you are planning an upgrade for nine months from now, today is a great time to get the DRAM and GPU orders going. Don’t wait. Get to know your vendors better. Find out when they plan to have inventory and try to put your organization at the front of the line. A related tactic is to avoid reactive buying, so don’t panic-buy at the peak season.

Patterns can help, as well. By standardizing interoperable server configurations, you make it simpler to order the components you need. In addition, you can order critical components even if they are not specially required for a project and create a strategic inventory. This can be tough to plan, but given the price and availability circumstances, it may be possible to get budget for such buys.

Overcome Hardware and Memory Shortages with Park Place Technologies

With widespread global hardware shortages and hardware prices soaring greater than 50% compared to last year, now is the time to consider alternative solutions to overcome this problem, which is on every IT manager’s mind.

Park Place Technologies is your partner to help you navigate this memory shortage, and there are three key strategies where we can help:

1. Get more years out of your existing IT hardware, with our third-party maintenance This extends to GPU server support.
2. Own quality pre-owned data center equipment that stretches your budget further.
3. Migrate to our IaaS platform, with AI-ready compute, that allows you flexibility to grow.