AI Servers: Which AI Hardware You Need for Training and Inference

An AI server is a server that manufacturers build specifically for artificial intelligence workloads. Specialized accelerators, usually several GPUs, do the computing. To keep them busy, the server needs very fast GPU memory, fast connections between the GPUs, and enough power and cooling for a lot of performance in a small space. Companies and research institutions use AI servers to train models, adapt them to their own data or run them in day-to-day operations.

A conventional server spreads many small tasks across relatively few but powerful CPU cores. It runs databases, email or virtual machines, for example. AI models compute differently: they multiply huge matrices of numbers, and thousands of cores working in parallel handle this fastest. That is exactly what GPUs do.

This is why an AI server looks different on the inside. It often holds four or eight GPUs, each with its own very fast memory. The system draws significantly more power and has to dissipate more heat.

In everyday use, AI server and GPU server usually mean the same thing. Strictly speaking, GPU server describes the design, AI server the purpose.

The GPUs deliver the computing power. The rest of the hardware has to keep up so they don't sit waiting for data. An AI server consists of these components:

• GPU: It runs the model. The number and type of GPUs determine which models a server can handle at all.
• GPU memory: Large data center GPUs use HBM (High Bandwidth Memory), for example the NVIDIA H200 with 141 GB at 4.8 TB/s bandwidth or the AMD MI350P with 144 GB at 4 TB/s. Smaller GPUs such as the NVIDIA RTX 6000 or AMD Radeon AI PRO R9700S get by with less expensive GDDR memory. If a model doesn't fit into the memory of one GPU, you split it across several.
• CPU and main memory: The CPU prepares data and controls the processes. If it is too weak, expensive GPUs sit idle.
• Interconnect: In training systems, NVLink (NVIDIA) or Infinity Fabric (AMD) typically links the GPUs within a server. Otherwise they exchange data via PCIe. Between servers, InfiniBand or fast Ethernet takes over. Learn more under NVIDIA Networking.
• Storage: Fast NVMe drives deliver the training data and save checkpoints.
• Power and cooling: Power supplies and cooling determine how much performance fits into one chassis.

What Types of AI Hardware Are There?

Besides GPUs, other chips accelerate AI models too. They differ in how flexible they are and where you find them:

• CPU: The general-purpose processor controls every system. Small models and simple inference also run on it without an accelerator.
• GPU: Graphics processors compute in parallel with thousands of cores and are flexible to program. They are the standard for training and inference in the data center.
• TPU and other ASICs: Cloud providers develop these specialized chips for their own platforms, Google the TPU, for example. Companies mostly use them as a cloud service, not in their own servers.
• NPU: Neural processing units sit in laptops, smartphones and edge devices. They run smaller models efficiently right on the device.
• FPGA: You can reprogram these chips after manufacturing. They suit special applications that need very short response times.

For AI servers in your own data center, there is hardly any way around GPUs. Common frameworks such as PyTorch and TensorFlow support them directly, and the software ecosystem around NVIDIA CUDA is the broadest.

The right configuration depends on what you plan to do with the server. Three workloads come up most often in practice:

Only a few companies train their own foundation models. Another path is more common: you take an open language model, adapt it and run it on your own hardware. In that case, memory and inference performance matter most.

Where Are AI Servers Used?

AI servers run wherever large volumes of data pile up and models need to deliver results fast. Typical fields of application:

• Research and universities: Research groups train models on language, image or measurement data and share the computing time of a central system.
• Automotive, mechanical engineering and manufacturing: AI inspects components through image recognition, evaluates sensor data for autonomous driving and predicts maintenance needs.
• Pharma, biotech and life sciences: Models predict protein structures, screen large numbers of potential drug candidates and analyze medical images.
• Climate research and meteorology: AI models complement classic simulations and calculate weather and climate scenarios much faster.

In many of these fields, the data is sensitive or very large. That is why research institutions and industrial companies in particular often run their AI servers in their own data center.

A single AI server typically holds up to eight GPUs. With the NVIDIA B300, that adds up to around 2.3 TB of GPU memory in one system. This is enough for a great many fine-tuning and inference projects.

You need a cluster as soon as a model or a training run exceeds the capacity of one server. Fast networks then connect many servers into one shared system. If the infrastructure keeps growing, it turns into an AI factory: a data center that a company builds and operates specifically for AI.

Many start with a single server. Even so, plan power, cooling and networking from day one so you can expand later.

AI servers pack a lot of power into just a few rack units. According to NVIDIA, a single NVIDIA H200 in SXM form factor draws up to 700 watts. Eight of these GPUs alone add up to 5.6 kilowatts, before CPUs, memory and fans. B300 GPUs in turn need up to 1,100 watts.

So check your data center before you buy:

• Power: Does the rack deliver enough power, and are the circuits rated for it?
• Cooling: Is air cooling still sufficient, or do you need liquid cooling?
• Space and weight: Can the rack carry the systems, and is there room left for later expansion?

For dense GPU systems, liquid cooling is often the better choice. It removes heat more efficiently, and you can reuse the waste heat more easily, for example for heating.

The cloud suits tests, short projects and fluctuating workloads, because you only pay for the computing time you use. Your own AI server pays off when you keep it busy over the long term. Typical reasons to run it yourself:

• You process sensitive data, for example from research, medicine or product development.
• Your data and models must not leave your own data center.
• You want predictable costs instead of variable hourly rates.
• Your GPUs run under high load for months on end.

In practice, many combine both: they test new models in the cloud and run production applications on their own hardware.

How Much Does an AI Server Cost?

The GPUs usually make up the largest share of the purchase price. A server with one RTX PRO 6000 (currently around €15,000 per GPU) therefore sits in a completely different price range than a system with eight B300s (currently around €40,000 per GPU). Total costs fall into four blocks:

• Hardware: GPUs, CPUs, main memory, storage and network cards.
• Infrastructure: Rack, power connection, circuit protection and, for dense systems, liquid cooling.
• Operations: Electricity, maintenance and staff for administration and updates.
• Software: Operating system, cluster management and, if needed, licenses such as NVIDIA AI Enterprise. NVIDIA already includes a five-year subscription with the H200 NVL.

A simple calculation shows how much electricity matters. Running at full load around the clock, the eight H200 GPUs from the data center example alone consume roughly 49,000 kilowatt hours per year. This figure doesn't yet include CPUs, cooling and networking.

What Should You Look for When Buying an AI Server?

Clarify these points before you buy:

1. Workload: Do you want to train models, fine-tune them or only run them?
2. Model size: How large is your biggest model, and how much GPU memory does it need, including the KV cache?
3. GPU interconnect: Do you need NVLink for training, or is PCIe enough for inference?
4. Scaling: Can you later expand the server into a cluster over a fast network?
5. Data center: Do power, cooling and weight fit your rack?
6. Software: Do drivers, CUDA, container environment and management tools come preinstalled and matched to each other?
7. Service: What warranty, response times and on-site service does the vendor offer?
8. Testing: Can you test your own models on the hardware in advance?

Data sheets show peak values. Only measurements with real workloads show how fast an AI server actually runs your model. Response time and throughput change considerably with model size, context length and the number of concurrent requests.

This is exactly what the GPU benchmark report RTX PRO 6000 vs. H200 measures. In it, MEGWARE compares both GPUs in servers with two cards each. The team tested LLM inference with Qwen3-32B, Llama-3.3-70B and a large mixture-of-experts model that activates only part of its parameters per request. The report documents the methodology and system configuration.

MEGWARE is an NVIDIA Elite Partner and builds AI servers and GPU clusters for research and industry. Before you buy, you can test your own models on the hardware in question at the MEGWARE Benchmark Center.

• NVIDIA: H200 Tensor Core GPU, technical specifications
• NVIDIA: HGX AI Factory, Enterprise Reference Architecture
• NVIDIA: RTX PRO 6000 Blackwell Server Edition