Large Model Concept Stocks Watch | Mininglamp Technology-W (02718.HK) Continues to Rise in Morning Trading, Cumulative Weekly Gains Exceed 10%

NewTimeSpace News: As of 10:15 AM on May 22, 2026, Mininglamp Technology-W (02718.HK) continued to rise in morning trading, with its stock price hitting HK$261, an increase of 2.35%. Driven by market momentum surrounding large language models, the company’s share price has maintained a steady upward trend this week, recording a cumulative gain of over 10% so far.

Guotai Junan Securities noted in a recent research report that Agent-driven growth is leading to an explosive increase in tokens. The business models of large model vendors are gradually closing the loop, with revenue growth significantly outpacing the rise in training costs, leading to continuous improvement in gross margins. Kaiyuan Securities stated that innovation in AI, ranging from models to computing power and applications, continues unabated. Model innovation has entered a stage of orderly and high-quality development, with AI model capabilities consistently improving; thus, commercialization will be a critical theme for large model companies in 2026.

Mininglamp Technology-W (02718.HK): As a prominent large model concept stock, Mininglamp Technology is recognized by the market as the "first Agentic AI stock on the Hong Kong Stock Exchange." Its self-developed model portfolio—including the DeepMiner large model product line (the underlying engine), models such as Mano/Cito, and the Cider inference acceleration framework—is interconnected through the Octo platform layer, which serves as a hub for human-Agent collaboration. These are ultimately delivered in the form of Agentic Services, empowering the implementation of decision-making AI agents in industries such as marketing and mass consumer goods. Moving away from the "Scaling Up" path of monolithic large models, the company adopts a "Scaling Out" strategy through the collaboration of multiple specialized small models. This approach achieves accuracy that surpasses general-purpose models in vertical scenarios. Its core barriers lie in data from niche scenarios, specialized models, and continuous learning, while also building open-source, private-deployable, and white-box auditable Private AI infrastructure.

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