What are GPU Servers, and What is Their Purpose in Data Centers?

The explosive demand for AI has turned GPUs into the most critical hardware in modern computing. 

However, their true potential hinges on the often-overlooked fact that, in the background, the servers are what’s powering them. The supporting infrastructure is therefore the most critical element, without which GPUs can fail at delivering the performance AI workloads demand.

This reality forces businesses to rethink data center architecture. GPU servers require specialized designs, enhanced cooling, and optimized power distribution. Inefficient deployment can lead to bottlenecks, power inefficiencies, and wasted capacity, or if the infrastructure becomes overloaded, irreparable hardware failures can occur.

So what does it take to build a server ready for next-gen GPUs?

In this blog, we’ll unpack the differences between GPU servers and conventional servers. We'll look at the infrastructure challenges they introduce, and share some best practices companies can follow to prepare their data centers for the specific challenges of the AI era.

GPU Servers – Definition

A GPU server is a server that contains graphics processing units alongside traditional CPUs. These servers can have one GPU or more, and are built for workloads that need parallel computation, where processing thousands of operations concurrently is far more efficient than sequential execution. This makes them ideal for AI model training, where speed matters most, and depends on the possibility of performing several calculations at the same time.

Still, every GPU server relies on CPUs to handle basic server functions, like operating system management. The addition of GPUs provides specialized compute power for accelerated workloads, creating a hybrid system capable of both “general-purpose” computing and executing high-performance parallel tasks.

The Growing Role of GPU Servers in Data Centers

Before the surge of generative AI, GPU servers were a rare sight in most data centers. The typical server environment was dominated by CPU-based servers, which have long served enterprise computing. CPUs are highly efficient at managing traditional workloads like database operations, web hosting, and a wide range of applications.

In recent years, however, the rise of AI-driven technologies has begun to reshape these environments. Training and deploying advanced machine learning models demand far greater compute power and parallel processing abilities than CPUs are capable of on their own. So, hosting GPU servers has become necessary. These are purpose-built for handling vast datasets and accelerating complex calculations, and as such, they are becoming indispensable for data centers that want to meet the needs of AI workloads. As this trend accelerates, GPU servers are becoming an essential component in data center infrastructure strategies.

Comparing GPU Servers vs. CPU Servers

The design priorities of GPU servers are very different from those of traditional CPU-only servers. Seemingly, they can occupy the same racks and connect to the same networking and power infrastructure, but the way they handle power, heat, and expansion makes them very different, and much more suitable for intensive workloads. GPU servers are equipped to handle the compute-heavy demands of AI training, complex simulations, and large-scale data analytics, with which CPU-only servers would quickly end up in bottlenecks.

Higher Power Requirements

The elevated power draw of GPUs also means that GPU servers must be able to deliver more energy overall. Some GPUs pull power through the motherboard, while others—especially high-end enterprise models—need dedicated connectors. This increased demand extends to the rack itself, which must be capable of supporting higher electrical throughput.

Expansion Options

GPU servers are designed from the ground up to host multiple graphics processing units, which means they require a greater number of PCIe slots than standard servers. Traditional CPU-based servers often include only a few expansion slots because they rarely need additional cards. GPU servers, by contrast, can support large numbers of accelerators—sometimes 10 or more in a single chassis—allowing for much higher compute density.

Better Cooling Abilities

Packing in that many GPUs leads to a significant increase in heat output, so GPU servers must employ superior thermal management. They typically feature more robust fans, larger heat sinks, and improved airflow designs, and in some enterprise setups, even liquid cooling systems. Without this, maintaining performance under heavy loads would be impossible.

Getting Your Data Center Ready for GPU Servers

Deploying GPU servers in a data center isn’t as simple as sliding them into existing racks and powering them on. They may be physically compatible with standard enclosures; however, GPU servers bring unique operational demands that require a lot of planning and some investment as well. 

These are the key areas to address when getting your facility ready for high-performance GPU servers.

1. Power Provisioning

GPU servers consume far more energy than traditional CPU-only versions. The increased demand these machines put on data centers can strain the existing electrical infrastructure if those are not big enough to handle the increased demand. To prevent this, data centers have to carefully evaluate their total power capacity and distribution systems and make sure that they can sustain the higher draw, even during peak times. To prevent overloads and the resulting failures, data center operators should consider upgrading their power delivery equipment before deploying GPU-intensive workloads. 

Because GPUs are notoriously power-hungry, operators should also consider how this impacts energy efficiency and sustainability goals. Server consolidation is a good start to improve overall energy efficiency, but cooling upgrades or renewable sources can also help. Measuring the PUE of the data is a good method for keeping track of the facility's energy consumption patterns. 

2. Advanced Cooling Strategies

The curse of the increased power usage is that it comes with more heat. Standard air-cooling setups often fall short when racks are densely packed with GPU servers. In many cases, data centers need to upgrade to high-efficiency cooling techniques, like liquid cooling, and implement rear-door heat exchangers and containment systems to maintain safe operating temperatures. These solutions can significantly improve thermal management and reduce the risk of hardware failures caused by overheating.

3. Rethinking Disaster Recovery

GPU servers also introduce new considerations for disaster recovery planning. 

These devices are not only expensive but also highly specialized, making it harder to replace or failover workloads quickly. With generic CPU servers, you can simply move applications to another server. But with GPU servers, you can't do this without compatible hardware.

So, to mitigate downtime, operators should design DR plans that account for the unique dependencies of GPU workloads. This involves keeping spare units ready at hand, but also implementing GPU-aware backup solutions and using edge data centers with preconfigured failover systems.

4. Strategic Investment and Planning

Although GPU servers fit into standard racks, treating them as “drop-in” replacements for CPU servers is a mistake. For these units to function properly without risking issues, each aspect of the data center, from power and cooling to resilience and recovery, has to be adapted to support their requirements.

Addressing these needs helps operators integrate GPU servers smoothly and get the most out of their potential for AI, analytics, and other compute-heavy workloads without sacrificing efficiency or reliability.

5. Network Capacity and Bandwidth

Before starting to work with GPU servers, data centers should make sure that their core network architecture can handle the increased throughput so that GPU servers aren’t slowed by bottlenecks.

GPU workloads move far more data than typical CPU tasks. Because of this, network infrastructure has to be scaled proportionately. Already existing connections are often not designed to handle GPU demand, and may not provide enough bandwidth or low latency for these workloads. Upgrading to faster links (such as 100GbE or higher) and optimizing traffic flows is often necessary to avoid congestion. 

Looking Ahead

As GPU servers become central to powering AI, big data, and other compute-heavy workloads, data centers can face infrastructure challenges. To support these workloads effectively, many operators find themselves having to make adjustments and upgrades to support the demands of these workloads efficiently. 

In the future, facilities will likely adopt even more advanced power and cooling technologies to counter the ecological footprint of these high-demand workloads. Network infrastructures will also need to scale further as data volumes grow and latency requirements become more and more tight.

GPUs will slowly become more and more specialized, and this will create new challenges that will need attention. For now, adapting is still in an early stage, but as it progresses, disaster recovery strategies and hardware lifecycle planning will play a larger role. Data centers that anticipate these trends and invest strategically will be best positioned to deliver high performance and reliability in a future driven by GPU servers.