FingerMotion, Inc. Intends to Enter the Edge AI Inference Computing Market

About this update from Fingermotion, Inc.

Singapore, Singapore--(Newsfile Corp. - June 3, 2026) - FingerMotion, Inc. (NASDAQ: FNGR) ("FingerMotion" or the "Company"), a mobile services, data and technology company, today announced plans to expand its infrastructure strategy through the development of modular AI-focused edge computing facilities designed to support the growing demand for localized artificial intelligence processing and inference workloads.The initiative builds upon the Company's existing telecommunications and technology platform operations and represents a strategic extension of the Company's long-term infrastructure and data services roadmap. The Company believes the rapid adoption of AI is driving demand for localized, energy-efficient computing infrastructure that can process data closer to end users. Management emphasized that the Company's approach is focused on edge-based AI inference infrastructure rather than hyperscale cloud data center development."As AI adoption accelerates across industries, we believe demand for localized inference infrastructure will continue to grow," said Martin Shen, CEO of FingerMotion. "Our strategy is focused on developing scalable edge computing solutions that can be deployed efficiently and expanded as demand increases. Given our history of developing AI usage in our big data division, we view this initiative as a natural extension of our technology platform and a potential driver of long-term shareholder value."Targeting the Growing Edge AI Inference MarketThe Company believes AI inference demand is expected to accelerate significantly as businesses deploy AI-enabled applications across sectors including manufacturing, logistics, smart city systems, healthcare, transportation, and industrial automation.Unlike cloud AI facilities that require substantial capital investment and extended development timelines, the Company's intended infrastructure model is to utilize modular, self-contained AI compute units capable of incremental deployment based on customer demand and regional requirements. These self-contained AI computing units are expected to support distributed AI workloads requiring real-time or near real-time processing, particularly in environments where latency, bandwidth efficiency, and localized processing are critical.Modular Micro-Grid Powered InfrastructureThe Company's proposed infrastructure design incorporates modular data cen...

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