When companies began building data centers for AI workloads on a massive scale, it quickly became clear that everyone was doing it their own way. Some used certain protocols and connection schemes, while others used completely different ones. As a result, data centers have difficulty «talking» to each other, and any attempt to merge the infrastructure of different companies or regions turns into a headache for engineers.
This is precisely the problem addressed by the new international standard, developed with the participation of the South Korean telecommunications giant SK Telecom and approved by the International Telecommunication Union – a UN agency responsible for global technical standards in communications.
What's Happening with Data Centers Anyway?
AI systems require enormous computing power. Training large models, processing real-time queries, and storing and transferring data – all of this is handled by data centers. Moreover, the load is growing so fast that a single data center simply can't keep up, making it necessary to combine several.
Simply put, an AI data center (abbreviated as AIDC) is not just a large server. It's an entire ecosystem: compute nodes, storage systems, network infrastructure, and load management mechanisms. And when there are several such centers – in reality, there can be dozens scattered across different countries – the question arises: how do you properly interconnect them?
Until now, there was no single answer. Each manufacturer and operator played by its own rules.
A Standard as a Common Language
The new ITU-T standard, based on SK Telecom's architecture, establishes a unified framework for interconnecting AI data centers. To put it very simply, it's like a common language that all market players who decide to build compatible infrastructure are now required to «understand.»
The standard describes exactly how connections should be structured between components within an AIDC and between multiple such centers. It sets architectural requirements: which functional blocks must be present, how they interact, and which interfaces are used for data exchange.
It's not an assembly manual for specific hardware – rather, it's a conceptual blueprint that real-world solutions must adhere to. Equipment manufacturers, cloud providers, and telecommunications companies can now rely on it when designing their systems, confident that the result will be compatible with others' solutions.
Why This Is Important Right Now
The growth of AI workloads has made the issue of interconnection critically important. Previously, data centers most often operated as standalone units. Now, however, large-scale AI tasks – whether it's training a model or serving millions of users simultaneously – require a distributed infrastructure. This means that multiple data centers must work as a single, cohesive unit, transferring data and workloads among themselves without delays or errors.
Without standardization, doing this reliably and scalably is nearly impossible. Each new partner or region would mean a custom integration – a long, expensive, and unreliable process.
With the adoption of this standard, the landscape changes. Companies building AI infrastructure according to unified rules will be able to pool resources faster, reduce integration costs, and more easily scale their systems across borders.
What This Means for SK Telecom
For the company itself, having its architecture recognized as an international standard is a strong signal to the market. When a specific organization's approach becomes the basis for a global norm, it de facto means that this very approach will be replicated in infrastructure worldwide.
SK Telecom is actively developing its AI infrastructure division and is positioning itself not just as a telecom operator, but as a technology player with global ambitions. Participating in the development of international standards is one way to solidify this positioning in practice.
What's Still an Open Question?
The adoption of the standard is an important step, but it's not the final one. ITU-T standards are advisory in nature: their implementation depends on how actively manufacturers and operators begin to apply them in practice.
The transition from «standard adopted» to «standard widely used» is a separate process that takes years. Major market players may stick to their own proprietary solutions for a long time, especially if their infrastructure is already built on different principles.
Nevertheless, the very fact that a single international benchmark has emerged is a significant event for the industry, especially at a time when AI infrastructure is growing faster than the rules of the game can be established.
