Maximizing AI Performance – Choosing the Server as a Key Factor

In recent years, the server industry has undergone dramatic changes. In an era where artificial intelligence (AI) technologies are rapidly advancing, technology companies developing software-based computing products across industries such as medical devices, defense, and semiconductors must decide between using dedicated AI-optimized servers or general-purpose servers that can handle a wider range of applications, including AI. This is one of the most critical questions developers face when choosing the right server for their application, as each option has its pros and cons. Defining and selecting the optimal technological solution as early as possible is particularly important and can be a critical component in developing a successful product, while saving time and budget resources.

As a core part of the development process, tech companies often work with partners who help transform ideas into products, supporting them from the ideation stage through development to international market distribution. This allows the companies to focus on their intellectual property and software development, while the partner handles all supporting aspects that aren’t at the product’s core but are essential to its success, such as global customer access, cost, availability, product lifecycle, and service quality.

Dedicated AI Servers – Custom-Built

Dedicated AI servers are specifically designed to run AI applications like machine learning models, heavy data processing, and deep learning. Unlike general-purpose servers that may also handle tasks like websites and databases, AI-dedicated servers come pre-built with tailored hardware and cooling systems suited for handling massive data loads and intensive computation. They are built to support high workloads and precise technical requirements.

Typically, these servers include powerful GPUs, large volumes of RAM, fast storage, specialized software, and enhanced cooling. The difference between dedicated (customized) AI servers and general AI servers mainly lies in how the hardware and server architecture are customized for specific AI tasks.

The key advantages of these servers include:

• Optimal performance and high processing power – These servers offer superior performance for parallel processing and are tailored for intensive data processing and translating complex algorithms into real-world applications. They usually feature advanced hardware and work optimally for AI workloads such as real-time image, language, and video processing, or working with generative models like GPT or Stable Diffusion.
• Time savings and full control over data and the server – These servers are equipped with memory and bandwidth designed to handle the demands of working with complex models. This becomes crucial when the usage is expected to scale – such as building platforms for hundreds of thousands of users, particularly when AI is the core of the business.
• Speed and efficiency – They deliver significantly faster performance than general-purpose servers and offer built-in flexibility to handle growing workloads.

However, using dedicated servers requires a significant upfront investment due to the need for advanced components and the ongoing support and maintenance, which includes frequent updates, cooling, and backups. Additionally, these servers may be less suited for non-AI-related tasks and are not always flexible enough for diverse or general development needs.

General-Purpose Servers

In contrast to dedicated servers, general-purpose servers offer greater flexibility and can handle a variety of computing tasks beyond AI. These servers include standard hardware such as CPUs, GPUs, and regular RAM. Their main advantages:

Built-in flexibility – They allow a wide range of tasks (hosting, storage, database management, etc.) on the same infrastructure and support scalability and versatility across different organizational areas.

Lower cost – The hardware is less expensive since it isn’t pre-configured to support dedicated GPUs, making the overall project cost lower. This is ideal for organizations seeking cost-effective solutions and greater control over their budgets.

Simple maintenance – No special expertise is required, and existing support resources can be relied on.

Ease of installation and use – These systems are familiar and widely used, making them easier to deploy and operate.

That said, general-purpose AI servers have several drawbacks, especially when it comes to specific AI workloads. Although they can perform many tasks, they don’t always provide the required efficiency for intensive AI projects, particularly when it comes to processing large datasets, due to the following reasons:

Limited performance – For heavy computational tasks, performance is restricted because the CPU is standard and lacks specialized deep learning hardware. If AI use is intensive, a general-purpose server won’t deliver the high performance needed for machine learning or real-time processing.

Slower response time – Large AI tasks may take longer to complete. For example, if intensive computing is required, the general server may struggle with parallel processing, potentially affecting user experience and delaying critical projects.

General-purpose servers are suitable for most organizations, particularly those not focused on AI and in need of a stable and user-friendly infrastructure. This makes them ideal for companies working with simpler machine learning models and less intensive data processing. They are also suitable for early-stage projects where AI capabilities are still being explored and where there isn’t a need for heavy investment—ideal for startups, companies running experiments, those with fluctuating workloads, or those seeking basic AI capabilities without building a full infrastructure.

The AI landscape and server market are rapidly evolving, and choosing the right server depends on each organization’s specific needs and goals. Matching the server to the organization’s requirements and objectives is crucial. At this stage, a technology partner plays a key role in the success of AI projects in various aspects:

Defining technical and business needs – Gaining a deep understanding of the client’s goals and mapping requirements such as performance, budget, security, future growth, and more.

Designing a custom solution – Choosing between a dedicated or general-purpose server, or perhaps a hybrid solution.

Selecting and optimizing advanced components – Including GPU, memory, storage, network, and more—based on anticipated workloads.

Ongoing support and maintenance – An integration partner ensures the system remains functional over time, with the option for upgrades, monitoring, and technical support.

**Saar Blitz is CVP of Technology at HIPER Global**

**Based on the article that was published in “Techtime” Magazine on April 28, 2025