Confirmation of Practical Performance According to Workload Characteristics in Remote Distributed AI Infrastructure Demonstration Between Tokyo and Fukuoka Utilizing 'IOWN APN'
Four companies—GMO Internet, NTT East, NTT West, and QTnet—have completed a technical demonstration of a remote distributed AI infrastructure between Tokyo and Fukuoka using IOWN APN. They confirmed that for large language model (LLM) training, performance degradation was limited to approximately 0.5% compared to local environments, and practical-level AI development is possible in a remote distributed environment.
📋 Article Processing Timeline
- 📰 Published: March 30, 2026 at 19:30
- 🔍 Collected: March 30, 2026 at 22:56 (3h 26m after Published)
- 🤖 AI Analyzed: April 22, 2026 at 22:35 (551h 39m after Collected)
GMO Internet, Inc. (Headquarters: Shibuya-ku, Tokyo; Representative Director, President and Executive Officer: Tadashi Ito; hereinafter, GMO Internet) of GMO Internet Group, NTT East Corporation (Headquarters: Shinjuku-ku, Tokyo; Representative Director, President: Naoki Shibuya; hereinafter, NTT East), NTT West Corporation (Headquarters: Osaka-shi, Osaka; Representative Director, President: Ryota Kitamura; hereinafter, NTT West), and QTnet, Inc. (Headquarters: Fukuoka-shi, Fukuoka; Representative Director, President: Yoshio Ogura; hereinafter, QTnet) have completed a technical demonstration of a remote distributed AI infrastructure between Tokyo and Fukuoka utilizing "IOWN (Innovative Optical and Wireless Network)" and its "APN (All-Photonics Network)".
In this demonstration, conducted from November 2025 to February 2026, a live IOWN APN line was laid between Tokyo (storage) and Fukuoka (GPU), and AI workload performance was measured and evaluated on an AI development platform connecting GMO GPU Cloud's GPUs with large-capacity storage. As a result, for large language model (LLM) training, performance degradation was limited to approximately 0.5% compared to local environments, confirming that the impact was extremely limited. For image classification tasks involving data loading, it was also confirmed that practical-level processing is possible even in a remote environment through optimization of training data, demonstrating that practical AI development in a remote distributed environment is possible with designs tailored to workload characteristics.
Prior to this demonstration, the four companies conducted a preliminary demonstration (Phase 1) in July 2025, performing performance tests in a simulated remote environment assuming a distance of approximately 1,000km between Tokyo and Fukuoka, and have published the details as a technical report.
Press Release: https://www.ntt-west.co.jp/news/2510/251002a.html
Technical Report: https://www.ntt-west.co.jp/news/2510/251002a_1.html
Based on the results of this demonstration, the four companies will continue to work towards the practical implementation of remote distributed AI infrastructure tailored to customer needs.
【Background and Purpose】
With the recent spread of generative AI and large language models (LLMs), the demand for AI development platforms has rapidly expanded. Traditionally, GPUs and large-capacity storage were required to be physically co-located. However, to address data center space constraints and the need for companies to manage data within their own premises, there is a demand for distributed AI development platforms that transcend geographical limitations. The four companies have been exploring the technical feasibility of connecting remote GPUs and storage using IOWN APN, which features high speed, large capacity, and low latency.