IOWN APN (All-Photonics Network) is a next-generation network technology that utilizes optical technologies to achieve high-speed, high-capacity, and low-latency communication, aiming to realize a more advanced and efficient network infrastructure.

What was the purpose of this technical demonstration?

The purpose was to demonstrate the feasibility and practical performance of a remote distributed AI infrastructure between Tokyo and Fukuoka using IOWN APN, connecting GPUs and large-capacity storage across geographical distances.

What were the key findings of the demonstration?

The demonstration confirmed that AI workloads, such as LLM training, experienced only a minimal performance degradation (around 0.5%) compared to local environments. Image classification tasks were also found to be practically feasible in the remote setup through data optimization.

Who were the participating companies?

The participating companies were GMO Internet, Inc., NTT East Corporation, NTT West Corporation, and QTnet Co., Ltd.

What are the implications of this demonstration for AI development?

This demonstration shows that it is possible to build practical and high-performance AI development infrastructure that is not limited by physical proximity, addressing data center space constraints and enabling distributed data management.

AI News NQ Analysis

GMO Internet、NTT東日本、NTT西日本、QTnet 運用『IOWN APN』實現東京至福岡遠端分散式AI基礎設施實證，確認符合工作負載特性的實用效能

NQ 評分 50/100

AI 摘要（NQ 加工版）

GMO Internet、NTT東日本、NTT西日本、QTnet 運用 IOWN APN 成功實證了東京至福岡之間遠端分散式AI基礎設施的實用性。

尚無 AI 分析資料。

常見問題

Q: What is IOWN APN?: A: IOWN APN (All-Photonics Network) is a next-generation network technology that utilizes optical technologies to achieve high-speed, high-capacity, and low-latency communication, aiming to realize a more advanced and efficient network infrastructure.
Q: What was the purpose of this technical demonstration?: A: The purpose was to demonstrate the feasibility and practical performance of a remote distributed AI infrastructure between Tokyo and Fukuoka using IOWN APN, connecting GPUs and large-capacity storage across geographical distances.
Q: What were the key findings of the demonstration?: A: The demonstration confirmed that AI workloads, such as LLM training, experienced only a minimal performance degradation (around 0.5%) compared to local environments. Image classification tasks were also found to be practically feasible in the remote setup through data optimization.
Q: Who were the participating companies?: A: The participating companies were GMO Internet, Inc., NTT East Corporation, NTT West Corporation, and QTnet Co., Ltd.
Q: What are the implications of this demonstration for AI development?: A: This demonstration shows that it is possible to build practical and high-performance AI development infrastructure that is not limited by physical proximity, addressing data center space constraints and enabling distributed data management.