[Photo: Namu Technology]

Namu Technology, a cloud and virtualisation solutions company, said on Tuesday it was selected as a joint research institution for a GPU orchestration technology development project led by the Korea Electronics Technology Institute (KETI).

The project aims to secure technology to operate and manage GPU cluster resources efficiently for the characteristics of AI workloads, and will be carried out with a total budget of 24 billion won. Namu Technology will participate in detailed research and development and take charge of developing GPU resource operation technology.

Namu Technology focuses on developing GPU resource operation technology in a multi-workload environment. It aims to raise resource utilisation and reduce operational complexity in environments where AI tasks with different characteristics run simultaneously. It will work to secure operation technology that enables stable and flexible use of GPU resources by focusing on workload characteristic-based resource allocation and improving cluster operation efficiency.

Based on its cloud and infrastructure operation technology, Namu Technology has accumulated capabilities in resource management and automation in virtualisation and multi-cloud environments, and will use the project as an opportunity to expand the scope of its technology application to the GPU-centric AI infrastructure area.

As demand for GPU infrastructure rises rapidly amid the spread of generative AI, the importance of technology to improve operational efficiency beyond simply securing resources is growing.

A Namu Technology official said, "This project is meaningful in securing foundational technology that can operate GPU resources efficiently in various AI workload environments." The official added, "Based on our cloud and infrastructure operation experience, we will advance related technologies and continue to expand our competitiveness in the AI infrastructure operations area."

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#Namu Technology #Korea Electronics Technology Institute #KETI #GPU orchestration #AI workloads
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