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Neural Network Servers: Don’t Let Your AI Project Crash

Neural Network Servers: Don’t Let Your AI Project Crash

The foundation of any project that requires artificial intelligence is its infrastructure and hardware. On weak servers it is impossible either to train a neural network or to allow it to operate stably and quickly. At the same time, loading and response speed is critically important for users. According to Google, a delay of just 100 milliseconds already has a negative impact on both user satisfaction and conversion rates.

Let’s take a look at what types of servers exist and which are best suited for neural networks.

Types of Servers

Map of different serversMap of different servers

Virtual Private Server (VPS)

A virtual private server is a part of a physical machine that is divided into multiple segments. Each isolated segment is essentially a small standalone server with its own operating system.

This model is widely used thanks to virtualization technology, which makes it possible to distribute server resources among multiple users.

The main advantage of a VPS is affordability and flexibility. It can be deployed quickly, resources can be scaled up, and it can be used for most standard tasks: websites, APIs, bots. However, the resources are still shared with other users, and for extremely high workloads such as machine learning and 3D rendering, a VPS is not the best option.

Dedicated server

A dedicated server belongs entirely to a single client. Unlike a VPS, there are no “neighbors” that could affect performance.

Dedicated servers provide maximum stability. This type of infrastructure is used where performance and system control are critical. Examples include large databases, ERP systems, streaming platforms, and production AI models under heavy load.

Main drawbacks:

  • High costs;
  • Need for administration.

Dedicated servers are best suited for large services with substantial budgets.

Cloud server

Cloud servers do not run on a single physical machine but on a cluster of servers. This allows resources to be scaled and quickly adapted to load at a relatively low cost.

Cloud servers are highly flexible. If one server fails, the load is redistributed among the others. This makes them well suited for startups, microservices, and distributed systems.

Main drawbacks:

  • Dependency on the provider;
  • Difficulty of migrating from one cloud to another;
  • Fluctuating pricing, like in subscription-based services.

GPU servers

GPU servers are especially powerful servers equipped with graphics accelerators designed for parallel computing. While most servers rely on CPUs, this is a different case.

GPU processors can perform thousands of operations simultaneously, making them orders of magnitude faster than CPUs for machine learning tasks. This is why neural networks are usually trained on GPUs.

Main drawback is the price. Modern models require tens of gigabytes of video memory and high bandwidth, which naturally increases cost. While a regular VPS may cost around 1,000 rubles per month, GPU servers are often billed daily — roughly 800 rubles per day.

AI Server Requirements

Training neural networks requires fast, parallel processing of large volumes of data. Therefore, the choice of processors is crucial.

CPUs handle sequential tasks well, while GPUs are optimized for parallel computation (neural networks are trained specifically on GPUs).

OpenAI indicates that the computational power used for AI training doubles every few months. As a result, AI models cannot realistically be trained without GPUs.

Video memory (VRAM)

The amount of VRAM directly limits model size and batch size. If memory is insufficient, the model either will not start or will run extremely slowly.

Hugging Face reports that large language models require tens of gigabytes of memory for inference.

RAM

RAM is used for data preprocessing and framework execution, while NVMe SSDs are used for fast dataset loading.

According to a Microsoft study, data read speed has a significant impact on model training time.

Training vs AI Inference Server

Training requires maximum computing power. GPUs are utilized close to 100%, multiple accelerators are used, large amounts of RAM and fast storage are required. This is the most expensive part of the infrastructure.

Inference is the execution of a trained model. Here, response time and stability are more important. It is often more cost-effective to use several weaker servers instead of one powerful machine to process more requests in parallel.

Buying vs Renting a Server

Initial cost Scalability Control Best use case
Renting servers Low Easy Managed by provider Variable workloads
Owned infrastructure High Expensive and slow 100% control Stable high workloads
Rented servers are easier to scale and expand, which is important for growing projects. Owned infrastructure, on the other hand, requires time and capital to expand. However, for most AI-driven projects, renting almost always wins.

How to Choose Hosting for AI Projects

In machine learning, the main limittion is usually the amount of video memory (VRAM) and the GPU’s ability to process data in parallel. If VRAM is insufficient, the model will either fail to start or operate extremely slowly. Therefore, choosing hosting should be based on the project’s goals, as training and inference require different types of servers.

Pricing is also a consideration. GPUs are expensive and scarce, so it is important to consider not only the hourly or daily rate but also additional costs such as traffic. A cheaper provider may turn out to be worse due to queues or instability.

Last but not least is configuration. Reliable hosting providers offer preinstalled drivers, CUDA, DDoS protection, and monitoring. For example, Cloud4box offers several preconfigured options for both small projects (8 GB) and extremely heavy workloads (48 GB). The hosting also allows custom configurations, enabling you to build a GPU server tailored to your needs.

How to Take a Good Care of Your AI

  1. Monitor bottlenecks. Even the most powerful GPU will not help if you have slow storage, a weak network, or poor load distribution. Industry experts directly point out that network and storage issues are becoming the main cause of neural network instability.
  2. Don’t leave your neural network alone. AI systems cannot simply be “launched and forgotten.” You must monitor both latency and resource utilization, as well as answer quality. For example, MLOps practices recommend tracking metrics such as P95 latency and resource utilization to identify bottlenecks and issues early. Otherwise, the system may appear to be functional while actually degrading.
  3. Adapt. AI services almost always operate under unstable traffic conditions, so flexibility is essential: automatic scaling during load spikes (autoscaling, which also removes unnecessary servers) and proper resource distribution.