What is an AI server, and how does it differ from a traditional virtual private server (VPS)?

AI server and VPS: key differences

An AI server is hardware designed for heavy mathematical computations: matrix operations, parallel processing of huge data sets, and the training and running of neural networks. Its main feature is that it utilises not only a central processing unit (CPU) but also specialised graphics processing units (GPUs), which handle a significant portion of the computations.

Essentially, the GPU in such a server is a kind of «graphics card», but not the sort typically installed in a home computer for gaming. AI servers use professional accelerators designed for round-the-clock operation with large volumes of computations and data. These solutions include the NVIDIA L40S, H100 and H200. For example, the H100 features 80 GB of specialised high-speed HBM3 memory, whilst the H200 has 141 GB of HBM3e. By comparison, the memory capacity and specifications of a standard graphics card can be quite different.

The difference between a CPU and a GPU can be easily illustrated using the analogy of a kitchen. A CPU is like a single head chef who can handle any complex dish with ease, but performs tasks sequentially. A GPU is like a large team of chefs: each can perform a simple task, but they all work simultaneously. When thousands of identical actions need to be carried out, this approach is significantly more efficient. This is precisely why a GPU is well-suited to the parallel computing inherent in neural networks.

However, a powerful GPU on its own does not make a server a fully-fledged AI server. All other hardware must also be up to the same standard. For example, a powerful accelerator requires sufficient RAM and a fast data transfer channel to the CPU. High-speed PCIe interfaces are used to connect GPUs, and in many GPU systems, specialised connections such as NVIDIA NVLink may be employed, which enable very fast data exchange between accelerators.

Storage devices are also important. AI models and datasets can be very large, and during training or when running a model, the server needs to read and write large amounts of data quickly. This is why AI systems use fast NVMe SSDs rather than slower storage devices, which could become a bottleneck for the entire system. NVIDIA’s reference configurations for AI servers include dedicated NVMe drives for each CPU socket, whilst even greater local storage capacity is recommended for training systems.

Another important component is the network. If a model runs on a single server, the network connectivity requirements may be more modest. However, when computations are distributed across multiple servers or a large array of GPUs, they need to exchange data very quickly. Such systems utilise specialised network adaptors and channels with speeds of hundreds of gigabits per second. For example, NVIDIA’s reference AI configurations use network adaptors with speeds of up to 400 Gbps.

Power supply and cooling must not be overlooked either. High-performance GPUs consume significantly more electricity than standard server components and generate a corresponding amount of heat. For example, in modern NVIDIA server platforms, a single GPU can be rated for power consumption of up to 1 kW. Consequently, AI servers require a more powerful power supply system and sophisticated cooling, whilst particularly dense configurations call for specialised heat dissipation solutions.

In other words, an AI server is not simply an ordinary server with a graphics card added to it. It is a balanced system in which the processors, GPUs, memory, storage devices, internal buses, networking, power supply and cooling all work as a single unit. And the more complex the task, the more important it is that none of these components limits the capabilities of the others.

A VPS (Virtual Private Server), on the other hand, is that very «ordinary kitchen» with a single head chef: a central processing unit that processes a wide variety of tasks sequentially. Websites, online shops, CRM and ERP systems, accounting software, email services, databases and other business applications run reliably on a VPS.

Unlike a physical server, a VPS is not a separate physical machine. It is a virtual environment created on a powerful physical server using specialised software (software). A single physical server can host several VPS instances simultaneously, but each one operates as a separate server with its own operating system, dedicated resources and settings. Users can install the necessary software, configure the server to suit their needs and manage it remotely.

VPS resources — CPU cores, RAM and disk space — are selected according to the workload. A basic configuration may be sufficient for a small website, whilst an online shop with a high volume of visitors or a business system will require more memory, processing power and fast SSD storage. If necessary, the VPS configuration can be changed to increase available resources without switching to dedicated physical hardware.

Therefore, a VPS is a versatile tool for most business tasks where stable operation of programmes and services is required, but there is no need for specialised GPUs for large-scale parallel computing. However, if the server needs to train or run complex AI models, process large data sets or perform other GPU-dependent tasks, a standard VPS may not be sufficient — in such cases, a dedicated AI infrastructure is required.

When an AI server is actually needed

• Training your own neural network from scratch or fine-tuning it using your own data. This is the most demanding scenario in terms of computing resources — this is precisely where an AI server with a powerful GPU is truly essential.
• Deploying a ready-made model on in-house hardware. A company takes a pre-trained model (for example, an open-source language model) and runs it on its own infrastructure — for the sake of data privacy or independence from external services. A GPU server is also required here, although the performance requirements may be lower than when training from scratch.
• Using off-the-shelf AI services via an API. This is the most common scenario: a business connects ChatGPT, Claude, Gemini or another neural network to its website or CRM via API requests. In this case, all the «heavy lifting» is done on the neural network provider’s side, whilst your server simply sends requests and receives responses. No GPU is required for this task — a standard VPS is sufficient.

It is precisely this third option that most businesses are choosing today: you can integrate a ready-made AI assistant into a website chatbot or automate the processing of customer enquiries without investing in expensive hardware.

The main differences between an AI server and a VPS

1. Type of computation. A VPS is designed for sequential, diverse tasks, whilst an AI server is designed for large-scale parallel operations.
2. The «heart» of the server. In a VPS, the CPU performs the bulk of the work, whilst in an AI server, it is the GPU.
3. Cost. Hardware with graphics accelerators is significantly more expensive due to the complexity of manufacturing and increased power and cooling requirements.
4. Availability. A VPS can be rented and deployed in a matter of minutes, whereas powerful GPU servers are generally a more expensive solution that takes longer to configure.

Which to choose: an AI server or a VPS?

If your aim is to implement a chatbot on your website, automate customer responses or add AI analytics to your CRM via the APIs of off-the-shelf services, there’s no point in overpaying for GPU hardware. A standard virtual server can handle such tasks with ease: it hosts the application code, which acts as a «bridge» between your business and an external neural network.

For such scenarios, renting a VDS/VPS from Maxnet is ideal — virtual servers powered by Intel Xeon Scalable processors with ECC memory and NVMe SSDs, offering a choice of operating system and flexible resource scaling to accommodate increasing workloads. This configuration is sufficient to reliably process requests to neural networks, keep your website and CRM running under load, and scale up capacity as needed — without the expense of «hardware» that you will most likely not need.

However, if you plan to train your own model or deploy a neural network locally for full control over your data, then it is worth calculating the cost of specialised GPU hardware separately, taking this specific scenario into account.

In most cases, AI for business isn’t about buying new hardware, but about the skilful integration of off-the-shelf solutions into your existing infrastructure. If you’re unsure which configuration is right for your needs, the Maxnet team will help you choose the best VDS/VPS option for your specific project.