System OEMs & Integrators
As workloads become more demanding, efficiency across the entire system becomes the key to performance
System design has become more difficult.
For many years, enterprise data centers were built around a set of familiar patterns. Servers, storage, and networking were deployed in relatively predictable configurations, and vendors delivered systems that were broadly similar in structure and behavior. Differences existed, but they were often incremental rather than fundamental.
That environment has changed.
System design is becoming more complex as workloads become more demanding—and more competitive as differentiation at the component level has largely disappeared.
Modern workloads, particularly those that have (or will have) AI in their workflows, rely heavily on accelerators, high memory bandwidth, fast interconnects, and highly parallel operations. These systems behave far more like supercomputers than traditional enterprise infrastructure. Whether the challenge comes from larger data sets, deeper CPU/GPU processing, heavier I/O demands, or all three at once, the result is the same: system design matters more than it used to.
At the same time, the competitive landscape has shifted.
Every system vendor now has access to essentially the same building blocks. Advanced CPUs, GPUs, high-speed interconnects, storage technologies, and memory configurations are widely available.
That has had a predictable effect: margins have been compressed, and traditional points of differentiation have largely disappeared.
On the surface, that might suggest that system design should be getting easier.
In reality, the opposite is happening.
With access to the same components, the number of possible design and configuration choices has expanded. As workloads become more demanding, the impact of those choices becomes more significant.
Small differences in how a system is designed, integrated, and operated can have a meaningful effect on performance, efficiency, scalability, and cost.
That raises an obvious question....
So What's the Best System?
There isn’t one.
The best system for your organization is the one that runs your specific mix of workloads in the most efficient manner possible and delivers the performance you need.
Every data center operates a different combination of workloads and has different priorities—performance, cost, energy efficiency, scalability, or some balance of all of them. That variability makes a universal “best system” impossible. What works well in one environment may be inefficient in another.
A system that performs well for one workload may be poorly matched to another. Differences in data size, processing patterns, memory access, and I/O behavior all influence how a system should be designed.
This is why system design cannot be separated from workload behavior.
Getting that match right is not always straightforward. Many organizations benefit from working with an experienced systems architect who can evaluate workload characteristics, identify the most appropriate configuration, and help navigate the trade-offs between performance, efficiency, and cost.
When systems are designed around how workloads actually behave, the results are very different.
Performance improves. Resource utilization increases. Power and cooling demands become more manageable. And the overall cost of delivering compute is reduced.
The "We Can Do Anything You Want" Problem
If there is no single best system, the next question is how organizations arrive at the systems they ultimately deploy.
In many cases, the process begins with a conversation that sounds something like this:
“What do you want?”
“We’re not entirely sure. Something that can handle these workloads and some new workflows.”
“We can do anything you want, just let us know.”
On the surface, this sounds like flexibility.
In practice, it shifts the burden of system design back onto the customer.
Most system vendors today have access to the same components and the ability to configure a wide range of solutions. That flexibility is real—but it does not guarantee that the resulting system will be well matched to the workloads it is intended to support.
In many cases, vendors are responding to stated requirements rather than actively shaping them.
The level of system-level design effort applied to a project often depends on the size and strategic importance of the deal. Large, high-visibility deployments may involve extensive architectural review, iteration, and optimization. Smaller deployments or less strategic engagements may not receive the same depth of architectural attention. In those cases, vendors are more likely to configure systems based on the information provided, rather than investing heavily in redefining the problem.
No one in this process is necessarily doing anything wrong, but the result is that the responsibility for defining “what should be built” is not as good as it should be.
Customers may not have the time, data, or experience to fully characterize their workloads. Vendors may not be positioned—or incentivized—to challenge assumptions or explore alternatives in depth.
As a result, systems are often designed around less than full understanding. Sure, they function, but they aren't fully aligned with how workloads actually behave.
As workloads become more complex and resource demands increase, the cost of that misalignment becomes more significant.
There's a Vendor for Every Need
Once you know what you need workload wise and have an idea of a rough configuration, it's time to get figure out who will design, assemble, and deliver the system (after you've vetted them and others, of course).
There is no single model for this.
Instead, organizations typically work with different types of system providers, each operating at a different level of the stack and with a different role in the overall process.
System OEMs
Original Equipment Manufacturers (OEMs) design and deliver complete systems based on standard architectures.
These vendors build systems using widely available components—CPUs, GPUs, interconnects, memory, and storage—and package them into validated configurations that can be deployed at scale.
Because they operate at large scale, OEMs tend to emphasize:
- consistency
- supply chain efficiency
- standardized system designs
At the high end of the market, where deals are large and strategically important, OEMs will bring deeper engineering resources to bear. Requirements may be challenged, benchmarks run, and systems designed with a clearer understanding of how they will be used.
In other cases, the process may be more transactional. Systems are configured based on stated requirements, with less emphasis on rethinking how those requirements were defined.
System Integrators
System integrators operate between component vendors and finished systems.
Their role is to take available technologies and assemble them into systems that are more closely aligned with specific workloads and operational requirements.
Integrators often work more directly with customers on:
- system design
- configuration choices
- deployment
This is particularly important in cases where:
- workloads are complex
- requirements are not fully defined
- or the engagement does not justify deep involvement from a large OEM
Because of this, integrators can play an important role in translating available components into systems that are better matched to real-world use.
This does not mean that all integrators operate at the same level, or that all outcomes are equivalent. But it does explain why this layer exists, and why it becomes more relevant as system complexity increases.
Choosing the Right Approach
Each of these approaches has strengths and trade-offs.
OEMs provide scale and standardization.
Integrators provide flexibility and closer alignment to specific workloads.
Platform vendors provide integration and operational simplicity.
The right choice depends on:
- workload characteristics
- internal expertise
- scale of deployment
- and how much system-level design guidance is required
As systems become more complex and the range of possible configurations continues to expand, the importance of selecting the right type of provider—and the right level of architectural involvement—becomes more significant.
A Curated View of the System Vendor Landscape
There are plenty of companies that can assemble servers and call it infrastructure. Most vendors—once they reach any meaningful scale—are working from the same set of components: CPUs, GPUs, high-speed interconnects, and large-scale storage.
The differences are not in the parts. They’re in how those parts are turned into a working system—how it’s designed for your workloads, how much real engineering attention your project gets, and how well it’s supported once it’s in production.
This table is not a comprehensive directory. It’s a curated list of vendors—global and regional—that have demonstrated the ability to actually deliver systems, not just talk about them.
Most AI and HPC systems are built from the same parts. What matters is who turns them into a system that actually works.
Category | Company | Description | Strengths / Typical Deals |
OEM | Dell Technologies | Designs and sells enterprise AI and HPC systems at global scale | ◆ Enterprise AI clusters◆ Large corporate deployments ◆ Strong sales + financing |
OEM | Eviden | Designs and delivers large-scale HPC systems including BullSequana platforms | ◆ European HPC ◆ Government systems ◆ Large custom supercomputers and AI systems |
OEM | Fujitsu | Develops HPC systems with deep engineering heritage and proprietary technologies | ◆ Japan HPC systems ◆ Government research ◆ Custom architectures |
OEM | Gigabyte | Designs and manufactures GPU servers and AI systems for data center deployments | ◆ GPU servers ◆ AI infrastructure ◆ ODM/OEM supply ◆ Mid-scale AI clusters |
OEM | Hewlett Packard Enterprise | Builds full HPC and AI systems including Cray-based architectures | ◆ National labs ◆ Large HPC/AI systems ◆ Liquid-cooled ◆ Supercomputers ◆ Custom designs ◆ Exascale |
OEM | Kaytus | Provides AI-focused GPU server platforms based on NVIDIA architectures | ◆ Asia AI builds ◆ GPU-dense systems ◆ Emerging AI infrastructure deals ◆ mid-scale OEM |
OEM | Lenovo | Supplies HPC and AI systems with strong presence in research and enterprise, Neptune liquid cooling | ◆ Academic HPC ◆ European research ◆ Balanced price/performance deals ◆ Custom supercomputers/AI |
OEM | Penguin Solutions | Designs, builds, deploys, manages, large-scale AI and HPC infrastructure systems, global coverage | ◆ Custom enterprise and research systems ◆ Managed AI infrastructure deployments ◆ Complex multi-vendor HPC builds |
OEM | Supermicro | Designs and manufactures GPU-dense servers and rack-scale AI systems, in-house liquid cooling | ◆ Often first to market with new hardware ◆ High density liquid cooled racks ◆ Rapid deployment of large GPU clusters |
OEM | 2CRSi | Designs, manufactures energy-efficient servers and HPC systems for AI and data center deployments | ◆ European HPC and AI systems ◆ Energy-efficient deployments ◆ Custom server designs ◆ Mid-scale cluster builds |
INTEGRATOR | ClusterVision | Designs and delivers HPC clusters and AI systems for research and enterprise | ◆ European HPC centers ◆ AI research clusters ◆ Turnkey HPC deployments ◆ Cross-border research projects |
INTEGRATOR | Colfax International | Designs and deploys custom HPC and AI clusters with deep performance engineering expertise | ◆ Performance-tuned AI training clusters ◆ Benchmarking-driven HPC deployments ◆ Research and advanced enterprise systems ◆ NVIDIA-based optimized builds |
INTEGRATOR | E4 Computer Engineering | European HPC integrator focused on custom cluster deployments | ◆ European research HPC systems ◆ Academic cluster deployments ◆ Custom AI/HPC builds ◆ Government-funded projects |
INTEGRATOR | MEGWARE | German HPC integrator delivering systems for research and industry | ◆ German HPC centers ◆ Industrial HPC deployments ◆ Research institutions ◆ CPU/GPU mixed clusters |
INTEGRATOR | Microway | Builds and supports HPC and AI infrastructure with emphasis on GPU-accelerated systems | ◆ GPU cluster deployments ◆ Research HPC systems ◆ Mid-scale enterprise AI clusters ◆ Turnkey HPC/AI builds |
INTEGRATOR | ParTec | HPC integrator and software provider focused on modular supercomputing systems | ◆ Modular supercomputing ◆ European HPC projects ◆ Software-driven systems |
INTEGRATOR | SourceCode | Builds custom AI and HPC clusters using multi-vendor components | ◆ Enterprise custom AI clusters ◆ Research HPC deployments ◆ High-touch engineering engagements ◆ Complex GPU cluster builds requiring tuning |
INTEGRATOR | Thinkmate | Provides custom-configured servers and HPC clusters for enterprise and research | ◆ SMB and mid-market HPC ◆ Custom server configurations ◆ Enterprise infrastructure projects ◆ Mid-scale deployments |
INTEGRATOR | World Wide Technology | Integrates large-scale enterprise IT and AI infrastructure solutions | ◆ Fortune 500 AI infrastructure projects ◆ Multi-vendor data center deployments ◆ Large integration-led deals ◆ Enterprise AI adoption at scale |
Nvidia: A Special Case
Nvidia doesn’t fit neatly into either category above.
It's not an OEM in the traditional sense, and it's not an integrator. Instead, Nvidia defines the architecture that both OEMs and integrators build around and often owns the system stack, relegating OEMs and integrators to essentially resellers.
Modern AI systems are increasingly based on NVIDIA reference designs—HGX and MGX platforms, NVLink fabrics, and tightly integrated software stacks. These designs extend beyond compute to include networking, with NVIDIA providing both InfiniBand and Ethernet as part of the overall system architecture.
Whenever you hear the term “AI factory,” it is a near certainty that NVIDIA is part of that solution.
This is reflected in the scale of Nvidia’s data center business. The company reported approximately $115 billion in Data Center revenue in 2025, spanning GPUs, InfiniBand and Ethernet networking, HGX/DGX-based systems, and software such as the NvidiaAI Enterprise suite.
Nvidia doesn't break out Data Center revenue by product category. But at this scale, and given what is included in that segment, Nvidia is effectively one of the largest system vendors in the industry—whether it is labeled that way or not.
At the same time, the company doesn't typically provide the full lifecycle services associated with traditional OEMs. Installation, on-site integration, and long-term break/fix support are still delivered by OEMs and integrators, and those capabilities remain critical to the success of any deployment.
One potential constraint on this model is customer concentration. A significant portion of Nvidia's data center revenue is driven by a very small number of very large customers—likely hyperscale cloud providers. These same companies are actively developing their own accelerator architectures, in part to improve economics and reduce long-term dependence on Nvidia.
As a result, many systems delivered today are, at their core, Nvidia systems including compute, interconnect, and software are implemented and supported by OEMs and integrators.
The differences between vendors still matter. But the architectural center of gravity has shifted.