A traditional data center protects the expensive hardware inside it with a “shell” constructed from steel and concrete. Constructing a data center’s shell is inexpensive compared to the cost of the hardware and infrastructure inside it, but it’s not trivial. It takes time for engineers to consider potential sites, apply for permits, and coordinate with construction contractors.
That’s a problem for those looking to quickly deploy AI hardware, which has led companies like Duos Edge AI and LG CNS to respond with a more modular approach. They use pre-fabricated, self-contained boxes that can be deployed in months instead of years. The boxes can operate alone or in tandem with others, providing the option to add more if required.
“I just came back from Nvidia’s GTC, and a lot of [companies] are sitting on their deployment because their data centers aren’t ready, or they can’t find the space,” said Doug Recker, CEO of Duos Edge AI. “We see the demand there, and we can deploy faster.”
GPUs shipped straight to you
Duos Edge AI’s modular compute pods are 55 feet long and 12.5 feet wide. Though they look similar to a shipping container, they’re actually a bit larger and designed primarily for transportation by truck. Each compute pod contains racks of GPUs much like those used in other data centers. Duos recently entered a deal with AI infrastructure company Hydra Host to deploy four pods with 576 GPUs per pod. That’s a total of 2,304 GPUs, with the option to later double the deployment to 4,608 GPUs.
Modular data centers aren’t new for Duos; the company previously deployed edge data centers for rural customers, such as the Amarillo, Texas school district. However, the pods for the Hydra Host deployment will be upgraded to handle more intense AI workloads. They’ll contain more racks, draw more power, and use liquid cooling to keep the GPUs running efficiently.
Across the Pacific, Korean technology giant LG is taking a similar approach. The company’s CNS subsidiary, which provides IT infrastructure and services, has announced the AI Modular Data Center which, like the Duos unit, contains racks of GPUs and supporting hardware in a pre-fabricated enclosure.
Also like Duos’ deployment, LG’s AI Modular Data Center contains 576 Nvidia GPUs with the option to scale up in the future. “We are currently developing an expanded version that can support more than 4,600 GPUs within a single unit, with a service launch planned within this year,” said Heon Hyeock Cho, vice president and head of the datacenter business unit at LG CNS. LG’s first Modular Data Center will roll out in the South Korean port city of Busan, where it could deploy up to 50 units.
LG and Duos are not alone. Hewlett Packard Enterprise, Vertiv, and Schneider Electric now have modular data centers available or in development. A report from market research firm Grand View Research estimates that the market for modular data centers could more than double by 2030.
On the grid, but under the radar
A modular data center site is quite different from traditional data center because there’s no need to construct a large steel-and-concrete shell. Instead, the site can be made ready by pouring a concrete pad. The pre-fabricated modules are delivered by truck, placed on the pad where desired, and then networked on-site.
Duos’ deployments, for instance, include power modules placed alongside the compute pods, and the pods are networked together with redundant fiber connections that allow the pods to operate in unison. Recker compared it to lining up school buses in a parking lot. “Everything is built off-site at a factory, and we can put it together like a jigsaw puzzle,” he said.
That simplicity is the point. Both Duos and LG CNS expect a modular data center can be deployed in about six months, compared to the roughly two or three years a conventional data center requires. Recker said that, for Duos, the turnaround is so quick that building the pre-fabricated unit isn’t always the constraint. While it’s possible to construct a pre-fabricated unit in 60 or 90 days, site preparation extends the timeline “because you can’t get the permits that fast.”
Modular data centers may also provide good value. Recker said a five-megawatt modular deployment can be built for about $25 million, and that Duos’ cost per megawatt is roughly half what larger facilities charge. For Duos, savings are possible in part because its modular data centers can target smaller deployments where the permitting is less complex. Smaller, modular deployments also meet less resistance from local governments, which are increasingly skeptical about data center construction.
While Duos targets smaller deployments, LG hopes to go big. Its planned Busan campus of 50 AI Modular Data Centers suggests an ambition to achieve deployments that rival the capacity of conventional facilities. A site with 50 units would bring the total number of GPUs to over 28,000. Here, the benefits of a modular approach could stem mostly from scalability, as a modular data center could start small and grow as required.
“By adopting a modular approach, the AI Modular Data Center can be incrementally expanded through the combination of dozens of AI Boxes,” Cho said. “It’s enabling the construction of even hyperscale-level AI data centers.”
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Matthew S. Smith is a freelance consumer technology journalist with 17 years of experience and the former Lead Reviews Editor at Digital Trends. An IEEE Spectrum Contributing Editor, he covers consumer tech with a focus on display innovations, artificial intelligence, and augmented reality. A vintage computing enthusiast, Matthew covers retro computers and computer games on his YouTube channel, Computer Gaming Yesterday.
Facts Only
Duos Edge AI and LG CNS are deploying modular data centers to accelerate AI hardware deployment.
Duos Edge AI’s compute pods are 55 feet long and 12.5 feet wide, each containing 576 GPUs.
Duos has a deal with Hydra Host to deploy four pods, totaling 2,304 GPUs, with an option to double capacity.
LG CNS’s AI Modular Data Center also contains 576 GPUs per unit, with plans to scale to over 4,600 GPUs per unit by 2024.
LG’s first deployment will be in Busan, South Korea, with potential for up to 50 units.
Modular data centers can be deployed in about six months, compared to two or three years for traditional data centers.
Duos claims a five-megawatt modular deployment costs around $25 million, with cost per megawatt roughly half that of larger facilities.
Site preparation for modular data centers involves pouring a concrete pad and networking the units on-site.
Hewlett Packard Enterprise, Vertiv, and Schneider Electric are also developing modular data centers.
The modular data center market is projected to more than double by 2030.
Duos previously deployed edge data centers for rural customers, such as the Amarillo, Texas school district.
LG’s modular data centers use liquid cooling to manage intense AI workloads.
Executive Summary
Full Take
The strongest version of this narrative highlights a genuine innovation in data center design, addressing the bottleneck of AI hardware deployment. Modular data centers offer speed, scalability, and cost efficiency, which are critical as demand for AI infrastructure surges. The article presents a credible case for why companies are turning to pre-fabricated solutions, with concrete examples from Duos Edge AI and LG CNS. However, the narrative leans heavily on industry perspectives without exploring potential drawbacks, such as long-term durability, maintenance challenges, or environmental impacts of modular units. The focus on speed and cost savings could overshadow questions about whether these solutions are sustainable or if they create new dependencies on proprietary hardware.
Patterns detected: none
The paradigm driving this narrative is the urgency to scale AI infrastructure rapidly, reflecting broader trends in tech where speed often trumps caution. The unstated assumption is that modular data centers are inherently superior, but their long-term viability remains untested. Historically, rapid industrial scaling has led to unforeseen consequences—think of the early days of cloud computing, where efficiency gains came with hidden costs like energy consumption and e-waste. Here, the beneficiaries are clearly companies needing quick AI deployment, while costs may be borne by communities hosting these units (e.g., noise, energy demands) or by future operators dealing with obsolescence.
For human agency, this shift could democratize access to AI infrastructure, but it also risks centralizing control in the hands of a few providers. Second-order consequences might include reduced investment in traditional data centers, potential job displacement in construction, or even geopolitical tensions if modular units become strategic assets.
Bridge questions: What are the environmental trade-offs of modular data centers compared to traditional ones? How might local communities respond to the rapid deployment of these units in their areas? What happens if modular data centers fail to scale as promised—could this create a new kind of infrastructure debt?
Counterstrike scan: If this were part of a coordinated influence campaign, the playbook would emphasize urgency ("AI deployment can't wait!") while downplaying risks, using industry voices to frame modular data centers as the only viable solution. The actual content aligns with this pattern but doesn’t cross into manipulation—it’s a straightforward industry trend piece. No red flags detected.
Sentinel — Human
This article appears to be human-written, showcasing a distinctive writing style and personal voice. However, it's important to note that the analysis cannot definitively rule out machine assistance or coordination.