Choosing a server for neural networks. What server parameters should I pay attention to for neural networks?
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Why do neural networks need powerful servers?
Neural networks are complex artificial intelligence models that process enormous volumes of data and perform millions of mathematical operations every second. To operate efficiently, they require significant computing power and fast memory access. When training neural networks, their parameters are constantly updated, requiring parallel processing of numerical data, which is provided by powerful server processors.
Machine learning models operate on gigabytes or even terabytes of data. Without sufficient RAM and a fast drive (SSD or NVMe), the training process can take hours or days. Graphics processing units (GPUs) accelerate model training by tens of times compared to conventional CPUs. This is why most AI systems are deployed on GPU-enabled servers.
Neural network servers ensure uninterrupted operation even under heavy loads and allow for easy resource scaling— by adding GPUs, increasing memory, or increasing disk space. It's also important that models require reliable storage, backup, and protection—all of which are provided by professional AI servers.
The difference between running a model locally and in the cloud
When developing or training neural networks, developers have two options: running them locally on their own hardware or running them on a remote hosting provider's server. Both options have their advantages and limitations, and the choice depends on the project's scale, budget, and performance requirements.
Local execution is when the model runs on your own computer or server. You have complete control over the hardware, system, and data. A constant internet connection is not required, and data privacy is maintained.
The disadvantages of this solution include the high cost of hardware (GPU, RAM, SSD), limited scalability, and the need to independently maintain the server (updates, cooling, security).
Working on a provider's remote server has several advantages. You rent the necessary resources and pay only for use. Easy scalability is available, allowing you to quickly increase resources as needed. The server is accessible from anywhere in the world, and ready-made environments for working with AI are available. It's important to note that this solution requires a stable internet connection. Local deployment is suitable for research or small projects where privacy is important.
AI hosting or renting a dedicated AI server with a graphics card is the optimal choice for teamwork, large-scale models, and rapid deployment without investing in hardware.
What types of servers are there for AI and neural networks?
The server for the neural network or servers for working with AI can be local or cloud-based (remote AI hosting).
Local servers are proprietary hardware installed at a company or lab. They are suitable for research, prototyping, and small neural networks. The user has complete control over the environment and can work without an internet connection.
Cloud servers allow you to rent remote GPU resources from specialized providers. This is the most convenient solution for
The choice between renting and buying depends on the scale and duration of the project:
– If you're testing models, working with prototypes, or running small tasks, it's better to rent a server. It's cheaper and more flexible.
If you have a constant workload, large AI teams, or your own data center, it makes sense to purchase your own equipment. Although the investment is higher, it pays off in the long run.
Let's compare these two solutions in our special table:
Criterion | Server rental | Purchasing your own equipment |
Initial costs | Minimum - pay only for using resources | High – you need to buy servers, GPUs, network equipment |
Scalability | Lightweight – you can quickly increase power | Limited - requires physical technology updates |
Maintenance | Compliant provider (updates, cooling, security) | User controlled (administrator needed) |
Availability | Access from anywhere in the world, convenient for the team | Works only locally or through your own network |
Speed of deployment | Instant - the ready environment can be launched in a few minutes | It takes time to set up, install and test |
Data security | Data is stored by the provider - additional protection is required | Full control over data and physical access |
Economic feasibility | Suitable for short or medium-sized projects | Effectively large AI systems |
Internet addiction | Needs a stable connection | Works autonomously |
Server parameters for neural networks
When choosing a server for training or deploying neural networks, it's important to consider a number of key hardware characteristics. These determine the speed of model training, operational stability, and future scalability.
CPU. It plays a key role in data preprocessing and preparing tasks for the GPU. Recommended models: AMD Ryzen, Intel Xeon, EPYC.
A graphics processing unit (GPU) is the key component for training models. Optimal options include NVIDIA RTX 3090, A100, L40S, etc. Key parameters include the number of CUDA cores, which determines parallel computing performance; VRAM capacity, which affects the size of models and data sets that can be processed simultaneously; and framework support (TensorFlow, PyTorch).
RAM . RAM is essential for processing large datasets. The minimum for training small models is 16–32 GB, with 64 GB+ optimal.
Disk space. Read/write speed is crucial. SSD or NVMe provide fast read and write speeds. Recommended storage ranges from 512 GB for basic tasks to several TBs for larger projects.
Network bandwidth and stability are essential for working with large datasets and remote users. Therefore, hosting providers' data centers with speeds of 1–10 Gbps are advantageous for AI projects, as a fast and stable network is essential for processing large datasets and working with remote users.
Additional parameters for selecting a neural network server also include several important considerations. First and foremost, this includes cooling and power consumption in the data center where the server is hosted, as stable operation under load is essential. Scalability of the dedicated server machine's resources—the ability to add GPUs or RAM in the future—is also essential.
Data storage security on a server should include regular backups and reliable protection against failures.
Server software environment for neural networks
In addition to powerful hardware, an AI server must be ready to work with popular AI frameworks. A properly configured environment determines the speed of model deployment, ease of team collaboration, and system stability. What should you consider before purchasing a neural network server?
✅ Pre-installed AI frameworks. Most AI servers come with machine learning libraries installed. For example, TensorFlow is used for training deep neural networks and supports GPU optimization. PyTorch is a flexible framework ideal for research and quick experimentation. Keras is a high-level library for building TensorFlow-based models. Having these frameworks out of the box or the ability to quickly install them on a remote server allows you to start working immediately after connecting to the server.
✅ Docker and Kubernetes support. Containerization is increasingly used for team collaboration, scalability, and environment repeatability. Docker allows you to create isolated environments for each project. Kubernetes manages resource distribution between containers, which is especially important when running multiple models simultaneously.
✅ Python environment . Python is the primary language of artificial intelligence, so AI servers must support the rapid creation of virtual environments. Pre-installed packages include NumPy, Pandas, Scikit-learn, and others; and compatibility with CUDA drivers for GPU acceleration. Having a ready-made Python environment significantly reduces infrastructure preparation time.
How to choose a server for a specific task?
The choice of a server for neural networks (AI servers) depends on the goals of model training, execution, or research experiments. Each type of workload requires its own priorities and server resources.
The model training server is the most demanding stage of neural network processing, where the main load falls on the graphics processor. Here, you should pay attention to a powerful GPU with 64 GB of RAM or more, fast SSD/NVMe storage (at least 1 TB), and a network with a throughput of 1–10 Gbps.
A server for model execution. In this case, stability and fast data access are especially important. Therefore, when choosing a server, look for a powerful CPU (Intel Xeon or AMD EPYC), sufficient RAM (16–32 GB), an SSD drive for low latency, and reliable continuous operation.
Research servers. If your goal is to conduct experiments, test architectures, or optimize models, environment flexibility and scalability are essential. In this case, consider one or more mid-range GPUs, 32–64 GB of RAM, support for Docker, Conda, and JupyterLab, and the ability to quickly change configurations (AI hosting or VPS).
Conclusion: Basic tips for choosing a server for AI
Servers for working with AI must meet the basic requirements discussed above. Let's outline the key considerations when choosing a server for a neural network.
- Check the GPU – the number of cores, amount, and type of VRAM directly affect the speed of model training.
- Consider temperature and power consumption – for long-term calculations, the server must have effective cooling and a stable power supply. You can obtain this information from the hosting provider when ordering the server.
- Choose a provider with 24/7 technical support – this is especially important for AI projects that operate continuously.
- Check the SLA (Service Level Agreement) – availability guarantees (99.9% or higher) mean the server will operate reliably without downtime.
- Plan for scaling in advance – if your models will grow in the future, choose a hosting service that allows you to easily add resources (GPU, RAM, storage).
The optimal AI server or server for a neural network is, above all, a balance between power, stability, and flexibility. If you're just starting out, renting AI hosting is a great start before investing in your own hardware.