Demonstration of Remote Distributed AI Infrastructure Between Tokyo and Fukuoka Using IOWN APN Confirms Practical Performance According to Workload Characteristics
Demonstration of the practicality of remote distributed AI infrastructure between Tokyo and Fukuoka using IOWN APN.
📋 Article Processing Timeline
- 📰 Published: March 30, 2026 at 19:30
- 🔍 Collected: March 30, 2026 at 22:56 (3h 26m after Published)
- 🤖 AI Analyzed: April 16, 2026 at 07:38 (392h 42m after Collected)
GMO Internet Group's GMO Internet, Inc. (Headquarters: Shibuya-ku, Tokyo; President and Group CEO: Masatoshi Ito; hereinafter referred to as GMO Internet), NTT East Corporation (Headquarters: Shinjuku-ku, Tokyo; President: Naoki Shibuya; hereinafter referred to as NTT East), NTT West Corporation (Headquarters: Osaka-shi, Osaka; President: Ryota Kitamura; hereinafter referred to as NTT West), and QTnet, Inc. (Headquarters: Fukuoka-shi, Fukuoka; President: Yoshio Ogura; hereinafter referred to as QTnet) have completed a technical demonstration of remote distributed AI infrastructure between Tokyo and Fukuoka utilizing the "APN (All-Photonics Network)" of "IOWN (Innovative Optical and Wireless Network)".
In this demonstration, conducted from November 2025 to February 2026, a live IOWN APN circuit was established between Tokyo (storage) and Fukuoka (GPU), and AI workload performance was measured and evaluated on an AI development platform connecting GPUs and large-capacity storage of "GMO GPU Cloud." As a result, for Large Language Model (LLM) training, a performance decrease of only approximately 0.5% was observed compared to local environments, confirming that the impact is extremely limited. For image classification tasks involving data loading, it was confirmed that practical-level processing is possible even in a remote environment through optimization of training data, etc., demonstrating that practical AI development is possible in a remote distributed environment through design tailored to workload characteristics.
Prior to this demonstration, the four companies conducted performance tests in a simulated remote environment assuming a Tokyo-Fukuoka distance (approximately 1,000 km) as a preliminary demonstration (Phase 1) in July 2025, and have published the details as a technical report.
Press Release: https://internet.gmo/news/article/88/
Technical Report: https://internet.gmo/news/article/87/
Based on the results of this demonstration, the four companies will continue to promote initiatives towards the practical application of remote distributed AI infrastructure that meets customer needs.
[Background and Objectives]
With the recent proliferation of generative AI and Large Language Models (LLMs), the demand for AI development infrastructure has rapidly expanded. Traditionally, GPUs and large-capacity storage have been required to be physically adjacent. However, to address data center space constraints and the need to manage data within one's own facilities, the realization of distributed AI development infrastructure that transcends geographical limitations is being sought. The four companies have been investigating the technical feasibility of connecting GPUs and storage in remote locations by leveraging the high-speed, high-capacity, and low-latency characteristics of IOWN APN.
▼Examples of Challenges in Building AI Development Infrastructure
FAQ
What is IOWN APN?
IOWN APN (All-Photonics Network) is a next-generation network infrastructure that utilizes optical technologies to achieve high-speed, high-capacity, and low-latency communication.
What was the purpose of this technical demonstration?
The purpose was to demonstrate the feasibility and practical performance of a remote distributed AI infrastructure connecting GPUs and storage over long distances (Tokyo to Fukuoka) using IOWN APN, addressing challenges like data center space constraints and data management needs.
What were the key findings of the demonstration?
The demonstration confirmed that AI workloads, such as LLM training and image classification, could be performed with minimal performance degradation (approx. 0.5% for LLM training) compared to local environments, proving the practical usability of remote distributed AI infrastructure.
Which companies were involved in this demonstration?
The demonstration was a collaboration between GMO Internet, Inc., NTT East Corporation, NTT West Corporation, and QTnet, Inc.
What are the implications of this demonstration for AI development?
This demonstration suggests that AI development can be effectively conducted in a distributed manner across geographically separate locations, offering greater flexibility and scalability for businesses and researchers.