Industry Tailwinds Coupled with Edge AI Application Layout; Mininglamp Technology (2718.HK) Surges Over 15% in Morning Trading

NewTimeSpace (newtimespace.com) News, On June 22, 2026, large model concept stocks in the Hong Kong stock market collectively strengthened. Mininglamp Technology (2718.HK) opened higher and continued to push upward during morning trading. As of 9:53 AM, the company's share price was quoted at HK$245.400, up 16.19%.

NewTimeSpace (newtimespace.com) News, On June 22, 2026, large model concept stocks in the Hong Kong stock market collectively strengthened. Mininglamp Technology (2718.HK) opened higher and continued to push upward during morning trading. As of 9:53 AM, the company's share price was quoted at HK$245.400, up 16.19%.

On the news front, the Shanghai Stock Exchange (SSE) recently issued the Application Guidelines No. 10 for Issuance and Listing Review Rules of the Shanghai Stock Exchange — Application of the Fifth Set of Listing Standards of the Science and Technology Innovation Board by Artificial Intelligence Large Model Enterprises. The guidelines consist of 15 articles and came into effect on the date of release. This expansion into the AI large model sector opens up a direct A-share listing channel for a massive number of high-quality large model enterprises that have not yet turned a profit but possess core technologies. Vertically industry-specific large model enterprises such as Mininglamp Technology (2718.HK) may also qualify under the fifth set of listing standards on the Science and Technology Innovation Board (STAR Market).

Entering June, Mininglamp Technology (2718.HK) has accelerated its layout in multiple large model applications. In early June, the company signed a memorandum of strategic cooperation with The Education University of Hong Kong to jointly build an "AI + Education" joint research platform, opening up AI tools such as DeepMiner, Octo, and CoCraft to extend edge-side AI capabilities into the fields of education and scientific research.

In mid-June, Mininglamp Technology signed a strategic cooperation framework agreement with PATEO Connect+. The two parties will integrate Mininglamp Technology's capabilities in AI large models and AI Agent suites with PATEO Connect+'s resources in edge-side hardware entry points, AI computing power infrastructure, and in-vehicle application scenarios. Utilizing token economics as the underlying commercial logic, the platform aims to open up a complete closed loop of "brand marketing budget — vehicle-side AI computing power consumption — user scenario-based consumption."

Soochow Securities pointed out that the development path of large models remains uncertain, and three potential scenarios may unfold in the future of the large model industry: (1) Closed-source models continue to lead and widen the generational gap, which will cause profits to continuously concentrate in AI hardware and leading model vendors; (2) Open-source models continue to catch up and narrow the generational gap, driving the commoditization of model capabilities and forming a multi-model layered routing landscape, where the gross margin of the model-layer APIs will face pressure while the application layer and infrastructure will benefit; (3) A breakthrough in a new model architecture emerges, which could give rise to a cohort of entirely brand-new enterprises.

MININGLAMP-W (02718.HK)As an LLM concept stock, Mininglamp Technology is recognized by the market as the "First Agentic AI Stock in Hong Kong." Its self-developed model cluster includes the DeepMiner LLM product line (underlying engine), models such as Mano/Cito, and the Cider inference acceleration framework. These are all interconnected by the Octo platform layer to serve as a human-Agent collaboration hub, ultimately delivered in the form of Agentic Services to empower the implementation of decision-making agents in industries like marketing and mass consumption. The company bypasses the "Scaling Up" route of monolithic LLMs, opting instead for a "Scaling Out" approach through the collaboration of multiple specialized small models, achieving precision that surpasses general models in vertical scenarios. Its core moats lie in granular scenario data, specialized models, and continuous learning, culminating in an open-source, privately deployable, and white-box auditable Private AI infrastructure.

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