Demonstration of Remote Distributed AI Infrastructure Between Tokyo and Fukuoka Using IOWN APN Confirms Practical Performance According to Workload Characteristics
Utilizing IOWN APN, the practicality of a remote distributed AI infrastructure between Tokyo and Fukuoka has been demonstrated.
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- 📰 Published: March 31, 2026 at 01:54
- 🔍 Collected: March 30, 2026 at 22:56
- 🤖 AI Analyzed: April 15, 2026 at 10:52 (371h 56m after Collected)
GMO Internet Group's GMO Internet, Inc. (Headquarters: Shibuya-ku, Tokyo; President and Representative Director: Masaru Ito; hereinafter referred to as GMO Internet), NTT East Corporation (Headquarters: Shinjuku-ku, Tokyo; President and Representative Director: Naoki Shibuya; hereinafter referred to as NTT East), NTT West Corporation (Headquarters: Osaka-shi, Osaka; President and Representative Director: Ryota Kitamura; hereinafter referred to as NTT West), and QTnet, Inc. (Headquarters: Fukuoka-shi, Fukuoka; President and Representative Director: Yoshio Ogura; hereinafter referred to as QTnet) have completed a technical demonstration of a remote distributed AI infrastructure between Tokyo and Fukuoka utilizing IOWN (Innovative Optical and Wireless Network)'s APN (All-Photonics Network).
In this demonstration, conducted from November 2025 to February 2026, a live IOWN APN circuit was established between Tokyo (storage) and Fukuoka (GPU). The performance of an AI development platform connecting GPUs and large-capacity storage of "GMO GPU Cloud" was measured and evaluated for AI workloads. As a result, for the training of Large Language Models (LLMs), a performance decrease of only approximately 0.5% was observed compared to local environments, confirming that the impact was 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. This demonstration proved 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 a performance test in a simulated remote environment assuming a distance of approximately 1,000 km between Tokyo and Fukuoka as a preliminary demonstration (Phase 1) in July 2025, and have published the details as a technical report.
Press Release: https://www.ntt-east.co.jp/release/detail/20251002_01.html
Technical Report: https://www.ntt-east.co.jp/release/detail/pdf/20251002_01_01.pdf
Based on the results of this demonstration, the four companies will continue to advance initiatives towards the practical application of remote distributed AI infrastructure in response to 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 are leveraging the IOWN APN, characterized by its high speed, large capacity, and low latency, to create a remote distributed AI 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, large capacity, and low latency, enabling new services and applications.
What was the purpose of the demonstration between Tokyo and Fukuoka?
The purpose was to demonstrate the feasibility and practical performance of a remote distributed AI infrastructure connecting GPUs in Fukuoka with storage in Tokyo, utilizing the IOWN APN, to address challenges like data center space limitations and the need for geographically dispersed AI development.
What were the key findings of the demonstration?
The demonstration confirmed that the performance degradation for LLM training was minimal (around 0.5% compared to local environments) and that practical AI development is possible in a remote distributed setting by optimizing for workload characteristics.
Who were the companies involved in this demonstration?
The companies involved were 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 performed effectively across geographically separated locations, offering greater flexibility and scalability for AI infrastructure by overcoming physical constraints.