MWC 2026: SK Telecom and Panmnesia Sign Partnership to Innovate AI Data Center Architecture, Enhancing Cost Efficiency and Performance

Panmnesia, an AI infrastructure link solution provider, announced the signing of a strategic Memorandum of Understanding (MOU) with SK Telecom, South Korea’s largest telco and a leading AI company.The agreement, signed at MWC26 in Barcelona, aims to jointly develop a CXL-based next-generation AI data center architecture.

As large-scale AI services continue to expand, data centers are investing heavily in massive deployments of high-performance GPUs, resulting in astronomical costs. Recognizing the need for sustainable scalability, SK Telecom and Panmnesia are focusing beyond simple GPU expansion to technologies that enable more efficient utilization of existing computing resources. Through this collaboration, the two companies aim to simultaneously improve cost efficiency and performance by innovating data center interconnect architecture based on Compute Express Link (CXL)* technology.

Background: Limitations of Modern AI Data Center Architectures
Modern AI data centers typically configure servers with fixed ratios of CPUs, GPUs, and memory. Multiple servers are connected via networks to form racks, and multiple racks are interconnected to build data centers. However, as AI models become increasingly diverse and larger in scale, this architecture faces limitations in terms of cost-to-performance efficiency.

To address these challenges, the two companies propose:

1. Breaking away from rigid, monolithic server architecture
2. Replacing traditional network-based interconnects with CXL

Challenge #1: Resource Inefficiency from Fixed Server Configurations
In conventional AI data centers, CPUs, GPUs, and memory are statically bundled within individual servers. As a result, unused resources in one server cannot easily be utilized by others. In particular, when memory capacity becomes insufficient, additional GPUs – often unnecessary – must be deployed alongside it, creating inefficiencies. This structure lowers GPU utilization rates and increases both capital and operational expenditures.

To solve this issue, SK Telecom and Panmnesia propose a disaggregated architecture in which computing resources are separated by type and flexibly composed as needed. Instead of being confined within servers, CPUs, GPUs, and memory are interconnected at the rack level through a CXL Fabric Switch†, operating as a unified system. By dynamically allocating only the resources required for each AI workload, this approach minimizes unnecessary resource waste and maximizes cost efficiency.

Challenge #2: Performance Degradation from Network Overhead
The companies will also improve computational efficiency by fundamentally changing the interconnect mechanism. In conventional AI data centers, GPU collective operations‡ – essential for large-scale AI training and inference – rely on general-purpose networks such as Ethernet. This process introduces data copies and software intervention, resulting in performance degradation.

To address this limitation, SK Telecom and Panmnesia will eliminate network involvement in computational paths and transition to CXL. By utilizing CXL, it is able to interconnect resources without traversing conventional networks.

At the core of this architecture is the Link Controller, an electronic component that can be integrated into CPUs, GPUs, AI accelerators, and memory devices. Within each device, it enables direct communication over CXL, replacing data transfer that previously required multiple data copies into simple memory access operations. Furthermore, the architecture enables GPU-to-GPU and GPU-to-memory communication without software intervention, significantly improving processing efficiency. As a result, AI data centers can deliver higher performance without increasing the number of GPUs.

Collaboration Details
Under this collaboration, SK Telecom will lead the design of an architecture optimized for real-world deployment, leveraging its large-scale AI data center construction and operational expertise, along with its experience in AI model development and commercialization.

Panmnesia will implement a CXL-Based AI Rack by applying its link solutions – including CXL Fabric Switches that serve as the core of physical connectivity and Link Controllers responsible for logical integration. Through this approach, the link architecture—previously confined within individual servers – will be extended beyond server boundaries to the rack level and above.

The two companies plan to validate the next-generation AI data center architecture by running real AI models and comprehensively evaluating GPU and memory utilization, latency, and throughput by the end of this year. Following this, they intend to conduct proof-of-concept deployments in large-scale AI data center environments and pursue commercialization and business expansion.

Executive Quotes
Suk Geun Chung, head of AI CIC, SK Telecom, stated, “The competitiveness of AI data centers now extends beyond GPU performance alone and depends on system-level optimization encompassing memory and data flow. This collaboration will help alleviate the structural bottleneck known as the ‘Memory Wall,’ where data movement and supply cannot keep pace with increasing computational performance, thereby enhancing both the performance and economic efficiency of AI data centers.”

Myoungsoo Jung, CEO, Panmnesia, said, “Next-generation AI infrastructure will be defined not by the performance of individual devices, but by the architecture created through diverse link semiconductors. Together with SK Telecom, we aim to present a high-efficiency AI data center model that will set a new standard in the global market.”

*CXL (Compute Express Link) is a high-speed, low-latency interconnect standard that organically connects CPUs, GPUs, and memory, enabling flexible expansion and utilization of computing resources beyond traditional server boundaries.
† Fabric Switch is a device that flexibly interconnects multiple system devices while managing data flow between them.
‡ GPU collective operations refer to the process by which multiple GPUs share and aggregate computational results, an essential component for large-scale AI training and inference.