The Dynamics of AI Data Centers and the Evolving Role of OEMs and ODMs
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Market Overview
The rise of Artificial Intelligence (AI)-driven applications has had a profound impact on the infrastructure demands of data centers. As AI becomes a staple of business operations, Data centers are seeing a surge in demand for high-performance compute, storage, and networking capabilities. The growing need for more compute-intensive workloads, such as training Large Language Models (LLMs) and multi-modal networks, has placed pressure on existing server designs. This is driving a need for Original Equipment Manufacturers (OEMs) and Original Device Manufacturers (ODMs) to develop more customized and high-performance infrastructure solutions.
Capital Expenditure (CAPEX) for deploying large AI accelerator clusters, alongside Operational Expenditure (OPEX) for power and cooling, are key drivers behind the push for higher levels of compute utilization. As AI workloads continue to grow, small improvements in efficiency can lead to considerable cost savings in data center operations. OEMs and ODMs play a crucial role in meeting the performance and scalability demands of hyperscalers and enterprise customers, while accelerating Time to Market (TTM) for new technologies.
In this context, ABI Research shares the key market dynamics shaping the development of AI data centers to help ecosystem players (chipset suppliers, data center operators, etc.) understand what is needed to achieve success.
“Building large AI clusters now requires more than the networking expertise to move data between accelerators. The power and cooling requirements of today’s compute dense AI servers featuring NVIDIA H100 or AMD MI300X Graphics Processing Units (GPUs), for example, are vast and take many end customers by surprise.” – Paul Schell, Industry Analyst at ABI Research
How NVIDIA, OEMs, and ODMs Interact
NVIDIA collaborates closely with OEMs and ODMs to integrate Graphics Processing Units (GPUs) into AI data centers. Tier One OEMs, such as Supermicro and Hewlett Packard Enterprise (HPE), leverage NVIDIA’s reference designs to scale quickly and optimize new technologies. ODMs, on the other hand, focus on customized setups for hyperscale cloud providers like Microsoft.
This collaboration ensures that eventual designs deliver the expected performance that would otherwise be more or less guaranteed under a highly optimized reference design, implemented primarily by OEMs.
Power and Cooling
Cooling is a critical challenge in AI data centers due to the high computational power required by GPUs and accelerators. Liquid cooling is becoming a preferred solution due to its higher efficiency compared to air cooling. Hybrid cooling strategies—combining liquid and air cooling—are also gaining traction as they offer flexibility and improve performance.
Companies like Vertiv and Penguin Solutions, with expertise in High-Performance Computing (HPC), are helping data centers implement effective cooling systems to manage the growing thermal demands of AI infrastructure.
Managed Service Wrappers
Managed service providers are essential for managing the complexity of AI data centers. Cluster management solutions like HPE’s Performance Cluster Manager and Penguin Solutions’ Scyld ClusterWare ensure efficient operation of AI hardware, minimizing the operational burden on enterprises.
These services help optimize AI clusters, manage performance, and ensure reliability. Given the rapid pace of AI hardware innovation, managed services allow businesses to focus on core activities, while ensuring their AI infrastructure operates at peak performance.
End-to-End Expertise and Offerings
End-to-end solution providers are capturing a larger share of the market by offering complete AI data center solutions, from design to deployment. Companies like Vertiv provide integrated services that include power, cooling, and networking, tailored to meet the specific needs of AI workloads.
Such end-to-end solutions streamline the deployment process, reducing complexity and accelerating TTM for businesses looking to scale their AI infrastructure efficiently. As demand for AI data centers increases, end-to-end offerings will play a key role in supporting the rapid growth of the sector.
Key Companies
Conclusion
AI data centers are becoming the backbone of modern computing infrastructure, supporting some of the most demanding workloads in the world. The collaboration between OEMs, ODMs, and chip vendors like NVIDIA is crucial for meeting the growing demand for AI compute power. As AI infrastructure continues to evolve, the integration of advanced cooling solutions, managed services, and end-to-end expertise will play an essential role in scaling AI data centers to meet future demands.
To learn more about the dynamics of AI data centers and the role of OEMs and ODMs, download the full AI Server OEM/ODM Market report from ABI Research.
