3 E Network Technology Group Limited (Nasdaq: MASK), a business-to-business information technology business solutions provider committed to becoming a next-generation artificial intelligence infrastructure solutions provider, has announced the initiation of a global vendor evaluation and Request for Proposal process for its multi-megawatt AI compute center in Mikkeli, Finland, according to a company announcement dated September 29, 2026.

Procurement Process and Technical Evaluation for AI Compute Center

The procurement process targets high-density server clusters, liquid cooling infrastructure, and core networking equipment. It follows the recent release of the Mikkeli Data Center Blueprint, the finalization of multi-megawatt power parameters, and the establishment of compute distribution channels, transitioning the facility into the practical phase of physical hardware selection and deployment.

The evaluation focuses on four technical requirements set out in the blueprint, covering rack power density and liquid cooling, GPU architecture compatibility and network topologies, storage clusters, and power delivery infrastructure.

120 kW+ Rack Power Density and Direct-to-Chip Liquid Cooling

To address escalating Thermal Design Power in Large Language Model training workloads, the company requires that proposed thermal management solutions support rack power densities of 120 kW and above. The requirement aligns with previously established thermal management objectives.

Given the physical constraints of traditional air-cooling architectures when managing next-generation high-power AI accelerators, the evaluation will prioritize advanced Direct-to-Chip liquid cooling technologies. This technology employs micro-channel cold plates affixed directly to the silicon core, utilizing high specific heat capacity to manage primary component heat.

According to the company, this standard aims to mitigate localized thermal hotspots, maintain thermodynamic stability during extended training cycles, extend hardware operational life, and optimize the facility's overall Power Usage Effectiveness.

GPU Architecture Compatibility and Non-Blocking Network Topologies

The RFP process prioritizes architectural compatibility and stress-test reliability as key evaluation metrics. The company's engineering teams are benchmarking technical requirements against the spatial, power delivery, and data throughput profiles of upcoming flagship AI accelerator architectures, including ecosystems based on Blackwell and Vera Rubin planning.

In line with the high-speed cluster objectives set in the blueprint, the company has specified high-performance standards for low-latency and non-blocking interconnectivity. Addressing the intensive data interaction demands of LLM training, the evaluation will focus on 800G and above Ethernet and InfiniBand-class leaf-spine topology solutions, aiming to optimize internal data flows and ensure large-scale GPU nodes operate in a highly synchronized environment.

All-Flash NVMe Storage and Parallel File Systems

In the multimodal model landscape, the company states that data transfer efficiency is as vital as underlying computational power. To overcome Data Input/Output bottlenecks during large-parameter model training and prevent GPU compute idle time, the company has designated all-flash NVMe over Fabrics storage arrays and high-performance parallel file systems as standard procurement criteria.

This specification requires the storage architecture to deliver high read throughputs at the terabytes-per-second level with microsecond latency. The system must also support efficient, concurrent model checkpointing capabilities, allowing rapid preservation of extensive model state data.

The company says this facilitates recovery from hardware interruptions, minimizes the loss of training progress, and maintains the operational efficiency of compute assets.

48V DC Power Evolution for Green Energy Integration

To integrate smoothly with the facility's green energy architecture and manage significant transient power spikes associated with new-generation AI chips, 3 E Network requires rack-level power delivery infrastructure to be compatible with and capable of evolving toward a 48V Direct Current busbar architecture.

Compared to traditional 12V setups, 48V power delivery lowers line current, which reduces transmission losses and improves end-to-end power conversion efficiency.

The accompanying intelligent Power Distribution Units must provide high conversion efficiency alongside integrated dynamic load balancing and precise energy monitoring.

According to the company, this intelligent power distribution design is intended to offer robust reliability for the underlying electrical grid when high-density clusters manage complex inference tasks or initiate large-scale training runs.

Strategic Outlook and Execution Plan

With the foundational power parameters and commercial distribution channels for the Finnish project established, 3 E Network's management team views this core hardware evaluation as a crucial phase in translating the theoretical blueprint into physical infrastructure.

By outlining technical specifications, including the 120 kW+ liquid cooling threshold, non-blocking networking, high-throughput I/O, and 48V power compatibility, the company has communicated clear deployment requirements to the hardware supply chain. 3 E Network states it is focused on building a robust, industrial-grade technological platform to support the computational demands of large-scale AI models.

In the subsequent evaluation period, the company will engage in detailed technical discussions with selected vendors to finalize the infrastructure matrix selection, accelerating the capital expenditure rollout and the practical commissioning of the Finnish project.

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