More and more companies in Romania are interested in testing and exploiting the potential of artificial intelligence technologies. Due to the inherent complexity of these projects, we can’t talk about AI applications without professional data centers.

The dependency relationship between AI applications and data centers is getting stronger every year. For example, in 2023, almost two-thirds of companies (64%) consider AI technologies important for increasing productivity (according to Forbes Advisor). A quarter of them already use them as a solution to compensate for staff shortages.

Companies’ growing appetite for AI is also visible in how organizations are investing in this technology. According to IDC, in 2023 the total value of purchases made by companies will be 154 billion dollars. Almost 27% more than last year. The rate of growth will remain at the same level for the next three years, with estimates being that in 2026 the threshold of 300 billion dollars will be exceeded.

To make this technological leap, however, companies need an infrastructure that meets specific performance requirements. It is a condition that few organizations in Romania can cover with internal resources. That’s why the vast majority of companies that are already testing or using AI applications are doing so from data centers that provide dedicated infrastructure. 

Why run AI applications in data centers

The reasons are clear, and looking for other solutions requires heavy investment, skill development and a costly and time-consuming trial & error learning curve. Here are the main reasons why calling data centers is the best choice:

  1. Increased processing power requirements

AI technology requires very large capacities of computing power to train learning models and run workloads. At the moment, however, very few local companies meet this condition, which is why they turn to data centers that can provide them with the resources they need. Data Centers with a dedicated offer have high-performance hardware equipment – such as Graphics Processing Units (GPU), Tensor Processing Units (TPU) or High-Performance Computing clusters. GPU and TPU work as hardware accelerators that ensure fast and efficient processing of AI algorithms, being specially optimized for artificial intelligence applications. HPC clusters use a combination of high-powered processors (CPUs), very fast memory modules, and specialized hardware (GPUs) in a distributed computing architecture to efficiently process large amounts of data. In turn, Deep Learning models and real-time AI applications require specialized computing accelerators such as Application-Specific Integrated Circuit (ASIC) or Field-Programmable Gate Arrays (FPGAs).

All these technologies require the development of in-house skills for implementation and operation and are difficult to amortize without intensive use. For this reason, most companies that test or use AI technology turn to data centers that provide them with the necessary resources and that can be scaled according to the evolution of needs.

  1. The need for fast storage equipment

Training Machine Learning algorithms and running AI applications requires not only large computing power, but also very large volumes of data. A data center can provide the optimal answer to these needs by ensuring not only the condition of the storage capacity of the data sets, but also that of the speed with which they can be read and written. High-speed storage devices are critical for Deep Learning models, machine learning algorithms, or real-time AI applications because they require fast rates of access and transfer of information.

And in this case, the logic is similar – it is cheaper, faster and more efficient to use a data center with dedicated resources for AI technology than to invest in high-speed and high-capacity storage devices. Data centers have high-performance storage equipment, as well as parallel storage systems, to handle the massive data sets used in AI applications. Complementary, it uses intelligent storage techniques and compression algorithms to maximize storage efficiency.

  1. High network data access speed

Artificial intelligence involves the rapid processing and analysis of large volumes of data, which requires high-speed, low-latency networks. To deliver the expected results, HPC clusters need a high-capacity, scalable and fault-free network to support the workloads. Slow networks create bottlenecks in the overall infrastructure, reducing the effectiveness of deployed AI applications.

A data center has, from the start, advanced network technologies, fiber optic connections and high-performance interconnect solutions that ensure fast and efficient data transfer, reducing latency and improving overall performance. Thus, end customers can benefit from the desired performance without additional retrofit costs.

  1. Rack-level energy density

Equipment for running AI applications has high demands on the power consumption area. On average, in a traditional data center the power consumption varies between 4 kW and 6 kW per rack. In an HPC cluster, it starts from 20 kW and can reach up to 60 kW per rack. It’s a difference that few data centers or in-house data rooms can cover. On the one hand, because the addition of power consumption generates a similar evolution of the value of the bills. On the other hand, because this would involve substantial costs with the restoration of the dedicated energy infrastructure.

And here the call to an external supplier is the optimal choice. A data center is designed from the ground up to provide scalability and flexibility on the power supply side, so as to ensure high energy densities on demand, without additional investment costs for the end user and with the possibility of achieving economies of scale.

  1. High cooling capacity

Increasing rack-level energy density and processing power generates a significant amount of heat. Which inevitably leads to an increase in server cooling requirements, requiring a redesign of the entire system. Again, additional costs result, which cannot be avoided because otherwise the life of equipment is reduced, performance is reduced and existing cooling equipment is overloaded, increasing the risk of downtime.

And from this perspective, the call to data centers capable of supporting AI applications is the optimal solution because it does not involve any additional investment in equipment, installation, operation and maintenance, but only the payment of the consumption bill. Which can be significantly lower, given that Data Centers use cooling systems with increased energy efficiency.

All the conditions listed above are met by the M247 offer, which through the Bucharest data center offers optimal conditions for data migration, testing and development of AI applications.

The M247 Data Center ensures the achievement of expected performance levels, in conditions of predictability of costs, without generating additional investments for end customers. The flexible infrastructure enables rapid scaling of compute and storage capacity as customer needs evolve, and the dedicated team provides a full range of required services, facilitating rapid time-to-value for AI applications.

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