Global Computing Power Service Demand and Industry Trends Amidst the International Landscape: An Insight Report

The global economy is rapidly transitioning into an intelligent era driven by artificial intelligence. Computing power, often termed the "new oil" of the digital economy, has become a critical metric for national competitiveness. In 2026, driven by the commercial deployment of large AI models, intensifying geopolitical maneuvering, and the acceleration of energy transition, the demand for computing power services is experiencing exponential growth. Consequently, the industry landscape is undergoing a profound transformation from "scale expansion" to "quality and efficiency enhancement." This report analyzes industry trends and core service demands through four dimensions: demand explosion, technological innovation, ecosystem competition, and risk challenges.

I. Demand Explosion: AI Drives the Leap from "Availability" to "Ultra-Large Scale"

In 2026, global demand for AI computing power is growing exponentially, with intelligent computing power officially surpassing general-purpose computing power to become the absolute dominant force in the market. Industry data indicates that global intelligent computing capacity has exceeded 12.8 EFLOPS, a year-on-year increase of 58%. A shortage of 25-30% exists for high-end computing resources, with public intelligent computing centers operating at near-full capacity. This explosion is driven by three key scenarios:

· Dual Drivers of Training and Inference: While training a single trillion-parameter model consumes tens of thousands of PFlops per day, the full-scale implementation of AI applications in 2026 has pushed inference computing power to account for over 70% of total demand. For instance, AI Agents consume 5-10 times more tokens per task than traditional dialogues.

· Accelerated Industry Penetration: Surging demand for real-time computing in manufacturing quality inspection, financial risk control, and medical imaging is driving the rapid deployment of edge computing nodes. In autonomous driving, for example, vehicle-road-cloud collaborative computing demand is growing at an annual rate of 60%.

· Policy and Capital Support: Capital expenditure from top global cloud providers is projected to exceed $600 billion in 2026, with over 60% allocated to AI computing infrastructure, supported by national strategies integrating computing networks into new infrastructure.

II. Technological Innovation: Localization and Green Computing as Key Breakthroughs

Facing challenges such as dependency on imported high-end chips and energy constraints, the industry is restructuring its supply chain through technological breakthroughs:

· Hardware Layer: Breakthroughs in Compute-in-Memory (CiM) and Chiplet technologies are redefining efficiency. Leading chip designers have reduced GPU power consumption by 42% under peak performance through CiM, while advanced Chiplet packaging is lowering R&D costs by 40%.

· Architecture Layer: Heterogeneous computing is becoming mainstream, with hybrid architectures combining CPU, GPU, DPU, and photonic chips. Photonic AI chips have demonstrated a 100-fold increase in energy efficiency for image recognition, marking the transition of optical computing from labs to industrial application.

· Energy Layer: The green computing revolution is accelerating. Liquid cooling penetration is expected to reach 35% in 2026, with immersion cooling achieving a Power Usage Effectiveness (PUE) of below 1.08. "Computing-Power-Energy Coordination" has become a global trend, mandating that new hub facilities utilize over 80% green energy.

III. Ecosystem Competition: From "Single-Point" to "Full-Stack" Capability

The global computing market has formed a tiered structure of "leadership at the top, innovation in the middle, and supplementation at the bottom." Competition has shifted from hardware specs to full-stack capabilities encompassing hardware, software, energy, and networks:

· Full-Stack Layout: Leading cloud providers, holding over 30% market share, are leveraging large models to improve AI development efficiency by 70%.

· Differentiated Vertical Competition: Specialized enterprises are gaining ground, with specific AI training chips capturing 17% of procurement by major internet firms.

· Service Model Innovation: Computing-as-a-Service (CaaS) is becoming the mainstream model. The computing leasing market is expected to double from its 2025 baseline, driven by the optimization of global computing networks.

IV. Risk Challenges: Autonomy and Sustainability

Despite the bright outlook, three major challenges persist:

· Technological Autonomy: The localization rate for advanced process chips (below 7nm) remains under 20%, with continued reliance on imports for high-end GPUs and EDA tools. Geopolitical risks are exacerbating supply chain uncertainties, extending delivery cycles for high-end chips to 6-12 months.

· Energy Balance: Data center electricity consumption is projected to reach 3% of total social consumption by 2030, necessitating urgent optimization of energy structures to prevent decoupling between computing growth and carbon control.

· Application Implementation: AI penetration in traditional industries remains below 20%, highlighting the need for industry-specific large models to solve practical pain points.

V. Conclusion and Outlook

In 2026, the computing industry is transitioning from a "scale race" to a new stage of "quality and efficiency enhancement." To occupy the commanding heights of the intelligent economy, global enterprises must focus on three strategic directions: accelerating R&D to overcome technological bottlenecks, deepening computing-power-energy coordination to promote green computing, and refining computing networks to lower barriers for SMEs. Computing power is poised to become a ubiquitous resource, like water and electricity, driving the global digital economy to new heights.