System OEMs & Integrators
The Force Multiplier for everything else
Workloads are becoming more computationally intense as AI becomes embedded into more applications and workflows. What used to be relatively predictable enterprise loads are evolving into mixed environments that include everything from lightweight AI-enhanced business apps to dense accelerator nodes running large models or complex simulations.
In this environment, the most efficient and highest-performing system is the one that is best configured to match the specific resource usage patterns of the workloads that will actually run on it. Not every workload uses CPU, memory, GPU/accelerator, network, and storage in the same amounts or ratios.
Every customer’s environment is different — no two are exactly alike. Many customers don’t fully understand how their current workloads consume system resources, and they have even less insight into how new AI-infused workloads will behave under different hardware configurations. We have advice on how to profile and better understand your workloads here.
While individual components (CPUs, GPUs/accelerators, networking, etc.) have not become truly commoditized, the same components — both low-end and high-end — are now widely available to OEMs and integrators of varying size and geography. This means many vendors can build systems that look very similar on paper. The real differentiation today happens at the system level — how thoughtfully those components are selected, integrated, cooled, powered, and optimized for your particular mix of workloads, power and cooling constraints, and operational needs.
The “We Can Do Anything You Want” Problem
A common issue in this market is that some vendors respond to this complexity by telling customers “we can do anything you want” and then shifting much of the design, configuration, and optimization responsibility back onto the buyer. This puts a heavy burden on organizations that often lack the deep benchmarking expertise or time to evaluate the many possible hardware and software combinations. The result is frequently a system that looks acceptable on a spreadsheet but delivers disappointing performance, efficiency, or utilization in real-world operation.
The real differentiation today happens at the system level — how thoughtfully those components are selected, integrated, cooled, powered, and optimized for your particular mix of workloads, power and cooling constraints, and operational needs.
OEMs vs Integrators
OEMs (Original Equipment Manufacturers) Large vendors that design and build standardized server platforms at scale. They maintain internal test systems, run extensive benchmarks, and have system architects who develop optimized “recipes” for different workload types. They can provide solid guidance and are particularly well-suited for very large deployments where scale, consistency, and global support matter most.
However, OEM architects are mostly focused on the biggest customers and the largest, most high-profile deals. They cannot give detailed, personalized attention to every customer and every deal.
Integrators (System Integrators) Specialized companies that focus on building highly customized or workload-optimized clusters. They have skilled architects who are scaled to work with smaller customers and deals. Many integrators also specialize in particular workload types and can provide highly customized environments that more closely match a customer’s unique requirements.
You don’t need full system architect attention for every system purchase. But for the important ones — where efficiency, performance, and long-term TCO really count — taking advantage of skilled architectural advice and counsel can make a significant difference.
System Vendor Landscape
The differences between vendors are rarely in the individual parts. They are in how those parts are turned into a well-balanced, efficient system for your specific environment.
Below is a curated overview of major on-prem system providers, separated into OEMs and Integrators. This table is designed to help you quickly see the general strengths and typical use cases for each vendor.
Category | Company | Key Differentiators | Best Suited For (On-Prem) |
OEM | Broad enterprise reach, strong global service & financing options, frequently partners with integrators like Penguin on large/complex AI deployments | Mid-to-large enterprise AI deployments and general-purpose server needs | |
OEM | Large-scale custom system design, strong European and government project experience, deep expertise in complex integrations | National labs, government, and large custom AI/HPC systems | |
OEM | Long engineering heritage, custom high-performance architectures, strong presence in Japan and Asia | Government research and Japan-centric HPC/AI deployments | |
OEM | Aggressive pricing, high-density GPU server designs, fast configuration turnaround | Cost-sensitive high-density AI server deployments | |
OEM | Strong HPC and supercomputing heritage (Cray), excellent liquid cooling integration, proven at exascale level | Very large-scale HPC/AI, government, and complex enterprise deployments | |
OEM | AI-focused GPU server platforms from Inspur, competitive pricing and high density | Cost-sensitive mid-to-large AI deployments, especially in Asia and emerging markets | |
OEM | Factory-integrated Neptune liquid cooling, strong performance tuning capabilities, rapid growth in AI servers | High-density liquid-cooled AI clusters and global deployments | |
OEM | Deep liquid cooling expertise, highly customizable platforms, strong focus on tailored AI/HPC solutions | Organizations needing custom high-density or specialized liquid-cooled systems | |
OEM | Very high configuration flexibility, rapid time-to-deployment, cost-competitive dense systems | Dense GPU/accelerator clusters and projects needing fast deployment | |
OEM | Energy-efficient custom server designs, strong focus on green IT and immersion cooling, international manufacturing footprint | European and international deployments prioritizing energy efficiency and custom builds | |
INTEGRATOR | Turnkey deployments for research institutions | European research and cross-border HPC/AI projects | |
INTEGRATOR | Deep benchmarking and optimization expertise | Performance-critical AI training and research clusters | |
INTEGRATOR | Strong academic and government project focus in Europe, custom configurations | European research institutions and government projects | |
INTEGRATOR | Solid track record in industrial and research deployments, custom HPC clusters | German and Central European HPC/AI systems | |
INTEGRATOR | Strong GPU focus, turnkey builds, solid technical support | Mid-scale enterprise and research GPU clusters | |
INTEGRATOR | HPC integrator and software provider focused on modular supercomputing systems | European modular supercomputing and research projects | |
INTEGRATOR | High-touch engineering and complex multi-vendor integration | Enterprise custom AI clusters requiring deep tuning and integration | |
INTEGRATOR | Flexible configurations and good support for mid-market customers | SMB and mid-market HPC/AI deployments | |
INTEGRATOR | Large-scale multi-vendor integration and enterprise project management | Fortune 500 and other large enterprise AI adoption projects |
Nvidia: A Special Case
Nvidia doesn’t fit neatly into either the OEM or Integrator category.
Instead, Nvidia has become the defining force in modern AI system architecture. Most current AI systems are built around NVIDIA reference designs — HGX and MGX platforms, NVLink fabrics, and tightly integrated software stacks. These designs frequently extend beyond compute to include networking, effectively making Nvidia the architectural center of gravity for the majority of deployments.
When people talk about building an “AI factory,” it is almost always a NVIDIA-centric solution at its core.
This dominance is reflected in Nvidia’s extraordinary data center revenue — approximately $115 billion in 2025 — which spans GPUs, networking, reference systems, and software. At this scale, Nvidia functions as one of the largest system vendors in the industry, even if it does not traditionally label itself as one.
At the same time, Nvidia does not provide full lifecycle services such as installation, on-site integration, and long-term support. Those responsibilities still fall to OEMs and integrators. However, Nvidia’s control over the core architecture, reference designs, and software stack has increasingly pushed many OEMs and integrators into a reseller-like role, compressing their margins and limiting their ability to differentiate.
One notable risk in this model is customer concentration. A significant portion of Nvidia’s revenue comes from a small number of very large hyperscale customers, many of whom are actively developing their own accelerator architectures to reduce long-term dependence on Nvidia.
The result is that today’s AI systems are, at their core, NVIDIA systems — with compute, interconnect, and software — that are implemented and supported by OEMs and integrators. The differences between those vendors still matter, but the architectural center of gravity has shifted heavily toward Nvidia, reshaping the economics and power dynamics across the entire supply chain.
How to Choose
Each approach has strengths and trade-offs. OEMs provide scale and standardization. Integrators provide flexibility and closer alignment to specific workloads.
The right choice depends on:
- Your workload characteristics
- Your internal expertise
- The scale of the deployment
- How much system-level design guidance you require
The more you know about your current and future workload mix, any constraints (like power or cooling), and the role these workloads will play in your organization before talking to a vendor, the better your outcome will be.