At the G20 Innovation Ministers' Meeting held in North Carolina on September 2nd, NVIDIA CEO Jensen Huang delivered a powerful message on AI's expanding role in the global economy, stating, "Ultimately, every nation must recognize that AI is a foundational infrastructure, just like water, roads, electricity, and the internet." He added that each country must construct its own AI framework to empower its researchers, students, industries, and startups with "digital intelligence."

Huang's remarks echo his ongoing theme of the "AI factory." Unlike traditional data centers that merely store and process information, these modern facilities are engineered to produce something new: they consume electricity and raw data, then utilize advanced computing, networking, storage, and software to generate tokens and intelligent services. Consequently, the construction effort isn't about building more server rooms or simply stacking up hardware; it's about creating a sustainable system for producing intelligence on a massive scale.

When it comes to deploying a large-scale AI cluster, a GPU is just a single component in a much larger puzzle. Success hinges on a complex web of elements including a stable power grid, high-density data center design, liquid cooling mechanisms, high-bandwidth network fabric, and sophisticated storage and scheduling capabilities. This is why assessing an AI infrastructure company requires more than just looking at its computational power. Factors like power sources, deployment timelines, operational stability, and the efficiency of token generation per unit of compute are now just as critical as raw performance numbers.

Building these AI systems is a monumental task. Companies with the ability to coordinate power, land, equipment, and network orchestration into a cohesive whole are rare. Looking at the current market landscape, two companies illustrate contrasting approaches to this challenge: Range Intelligent Computing Technology Group Company Limited (300442.SZ) and GBA AI COMP (01396).

Range Intelligent Computing Technology Group Company Limited (300442.SZ) represents what can be described as a "bottom-up" strategy. With years of experience in IDC (Internet Data Center) operations, it has accumulated essential, capital-intensive assets like data centers, power supplies, cooling systems, and maintenance capabilities—the very infrastructure that is very difficult to replicate from scratch. Instead of starting over, it's upgrading its existing facilities to support high-density AI computing. The financial results speak to the success of this approach: in the first half of 2026, its AIDC business generated RMB 1.995 billion in revenue, a 126.24% year-over-year increase that now accounts for more than half of its total revenue. The company is transitioning from a simple server rack provider to a corporation that systematically converts its data center foundation into integral production assets for the AI era.

Portraying a different route is GBA AI COMP (01396), which employs an "engineering-led" method that constructs and delivers a complete "factory" rather than building from existing resources. It has already delivered and operates over 50,000 P of FP16 dense computing power and has developed a robust "facility, hardware, and technology" delivery system. Their expertise covers not just the IDC, electricity, bandwidth, and maintenance but also the assembly of servers, storage, and network hardware, and the orchestration, scheduling, and tuning of large clusters. They are even expanding into heterogeneous computing scheduling and model deployment, with a clear vision to establish themselves as a "Token Factory." Free from legacy systems, they can design and execute a fully integrated infrastructure from the start.

While their paths differ—one building from a resource base upwards and the other delivering a full, integrated chain—both underscore the same principle: the need to organize scattered elements to create a dependable AI production facility. The real measure of an AI factory isn't just its construction cost but its economic output. A well-built cluster isn't the end goal; the focus must be on generating steady revenue, maintaining high utilization rates over its lifecycle, reducing power and operational expenses, and continuously improving computational efficiency. The future of AI competitiveness is shifting from securing raw computational capacity to mastering the efficiency with which that power is used.

Huang Renxun's repeated emphasis on infrastructure signals a major paradigm shift. The race is no longer just about algorithms and models; it's moving deep into the domain of physical and operational systems. For China, this means the ideal AI factory isn't a copy of a foreign model or a venture tied to one type of chip. It's about building a system that can adapt to diverse computing resources, scale efficiently, and consistently produce intelligence. As the industry's focus turns from sheer "computing power scale" to "computing output efficiency," the next competitive edge will be defined not by resource hoarding, but by token yield, energy efficiency, and operational excellence.