Yolanda Yitong Lin, Industry Analyst, XAI100 Inc.
Executive Summary
With the rapid development of artificial intelligence (AI) technology—particularly breakthroughs in generative AI and large language models—global demand for computing power has experienced explosive growth. Traditional centralized data center architectures can no longer meet the low-latency, high-bandwidth, and distributed processing needs of AI applications. Against this backdrop, distributed AI computing power centers have emerged as critical infrastructure supporting the AI era.
This research focuses on the global distributed AI computing power center industry from 2020 to 2025, providing a comprehensive analysis across dimensions including technological evolution, business models, market structure, policy environments, and vertical applications. It aims to deliver in-depth insights and strategic guidance for industry participants. The research scope covers core areas such as distributed AI infrastructure, edge computing nodes, and intelligent computing power networks, with a focus on demand characteristics and development trends in three key vertical application scenarios: healthcare, autonomous driving, and finance.
I. Technological Development: An Architectural Revolution from Centralization to Distribution
1.1 Distributed Evolution of Hardware Architecture
The hardware architecture of distributed AI computing power centers is undergoing a fundamental shift from single GPU clusters to heterogeneous distributed architectures. Modern AI research clusters typically adopt a three-tier design:
– The basic computing layer is equipped with dual-socket 4th Gen Intel Xeon Scalable or AMD EPYC 9004 series processors, paired with 2TB DDR5 memory and PCIe 5.0/6.0 interfaces;
– The acceleration layer integrates 8x NVIDIA H100 GPUs interconnected via NVLink 4.0;
– The dedicated processing layer incorporates advanced accelerators such as Graphcore IPUs, Cerebras wafer-scale engines, and Groq tensor processing units.
The core advantage of this heterogeneous architecture lies in resource pooling and dynamic allocation. Technological breakthroughs in 2025 show that PCIe-over-Fabric achieves sub-500ns latency between resource pools, silicon nitride waveguide photonic interconnection reaches 200Tbps rack-level bandwidth, and the CXL 3.0 protocol supports 1PB of shared memory space. Practice at the University of Milan demonstrates that GPU pooling technology can achieve 93% resource utilization, effectively solving the “lock-in” issue of traditional static clusters where resources are occupied for the entire duration of a training task.
In the field of dedicated chips, Neural Processing Units (NPUs) stand out as the optimal choice for edge AI inference, offering unique advantages when power consumption and size constraints are prioritized. Compared to GPUs, NPUs are optimized for neural network inference, with power consumption as low as 1–5W—making them ideal for mobile devices and IoT applications. The Neural Engine in Apple’s M2 chip delivers approximately 15.8 TOPS of inference performance while consuming only a fraction of the power of traditional GPUs.
1.2 Intelligent Innovation in Software Systems
The development of distributed AI training frameworks has gone through three key phases:
1. Parameter Server Era (2015–2018): Google proposed the Worker-Parameter Server separation design, supporting asynchronous updates;
2. AllReduce Era (2018–present): NVIDIA launched the Ring AllReduce algorithm combined with synchronous update strategies, with PyTorch DDP and Horovod becoming mainstream;
3. Hybrid Parallelism Era (2020–present): Synergistic development of three strategies—data parallelism, model parallelism, and hybrid parallelism.
Emerging frameworks in 2025 demonstrate stronger performance advantages:
– JAX: A functional + static optimization framework developed by Google, excels in large-model research with static optimization capabilities that make it 2–3x faster than PyTorch;
– Megatron-LM: An open-source large-scale model training framework by NVIDIA, serving as an industry performance benchmark;
– Colossal-AI: Preferred by domestic teams for its user-friendliness and robust optimization capabilities.
In terms of distributed training optimization:
– DeepSpeed enables memory-efficient training via the ZeRO optimizer, supporting trillion-parameter models;
– Frameworks like FSDP (Fully Sharded Data Parallel) and FairScale excel in addressing memory demands of large-scale neural networks and coordinating multi-GPU operations.
A common feature of these frameworks is their ability to significantly improve training efficiency and reduce costs through intelligent resource scheduling and communication optimization.
1.3 Breakthrough Progress in Network Technology
Network technology is a critical pillar of distributed AI computing power centers, with multiple breakthroughs between 2020 and 2025:
– 5G/6G Networks: 5G provides a high-speed, low-latency communication foundation for distributed AI, while the 6G vision aims to enable AI-powered network sensing, prediction, and self-optimization, creating a truly intelligent telecommunications ecosystem;
– NVIDIA Spectrum-XGS Ethernet: A landmark technology for cross-regional scaling, it can combine multiple distributed data centers into a gigawatt-scale AI superfactory. Through auto-adjusting long-distance congestion control, precise latency management, and end-to-end telemetry, it nearly doubles the performance of NVIDIA collective communication libraries;
– Optical Network Technology: China Information and Communications Technology (CICT) launched a full range of 1.6T optical modules covering short-to-medium-distance scenarios and demonstrated China’s first 2.0T silicon photonics chip. 800G transmission technology, capable of delivering 3 million high-definition movies per second, provides unprecedented bandwidth support for distributed AI training.
1.4 Deep Integration of Liquid Cooling and Heterogeneous Computing
As power consumption of AI chips continues to rise, traditional air cooling can no longer meet demands—making liquid cooling technology a standard rather than an option.
– Cold Plate Liquid Cooling: With advantages of easy deployment, low cost, and excellent heat dissipation, it has become the primary cooling method for future data centers, with retrofitting costs below 30%;
– Immersion Liquid Cooling: Delivers superior performance. Single-phase immersion solutions using media such as 3M fluorinated liquids support 100kW/cabinet heat dissipation, reducing PUE (Power Usage Effectiveness) to below 1.05. Google has deployed fully immersed racks in its Oklahoma data center, cutting cooling energy consumption by 90%;
– NVIDIA Blackwell Platform: Improves water efficiency by over 300x through liquid cooling technology.
In heterogeneous computing, data centers have fully transitioned to the “CPU + GPU + DPU + ASIC” heterogeneous model by 2025. NVIDIA’s next-generation H100 product will use 3nm process technology, with single-chip computing power exceeding 20 PFLOPS. Chips like Google TPU and Huawei Ascend, designed with memory-computing integration, have increased memory bandwidth by more than 5x.
1.5 Synergistic Development of Edge Computing and Distributed AI
The integration of edge computing and distributed AI is accelerating. The proportion of AI-driven edge computing applications has risen from 45% in 2023 to over 60% in 2025. Gartner predicts that by 2025, 75% of enterprise data will be processed at the edge, and the number of edge data centers will exceed that of traditional data centers by 3x.
The core feature of this integration is the formation of a three-tier “cloud-edge-end” architecture:
– The cloud handles large-model training (serving as the “R&D department”);
– The edge manages small-model inference (acting as the “execution department”);
– End devices are responsible for data collection and local processing.
Edge devices collect user feedback daily and transmit it back to the cloud; the cloud then updates models and distributes them to the edge, forming a closed-loop optimization system.
Technically, edge AI can reduce power consumption by up to 60% through specialized, energy-efficient hardware and optimized software architectures. This distributed approach is particularly well-suited for scenarios requiring ultra-low latency, such as autonomous driving and smart manufacturing.
II. Business Models: Diversified Profit Mechanisms and Cost Optimization Paths
2.1 Innovative Evolution of Operation Models
The operation model of distributed AI computing power centers is shifting from traditional centralization to decentralized, sharing economy models. The most common enterprise model concentrates training in ultra-large-scale data centers and distributes inference to edge locations—forming a “training center architecture”: clusters of 1,000+ GPUs in centralized facilities, interconnected via 800Gbps InfiniBand, and sharing Lustre/GPFS storage systems.
Emerging Model Mesh Networks represent the most radical architectural shift. This model transforms billions of idle consumer devices into a distributed supercomputer for collaborative AI model operation, reducing costs by 97%, achieving latency below 10ms, and ensuring complete data privacy. By 2030, an estimated 34 billion devices will participate in mesh networks, creating $847 billion in value.
In business model innovation, blockchain-based governance mechanisms and tokenized participation models are emerging. This approach ensures that AI scales not only faster but also more reliably and sustainably. Intelligent agents connect distributed computing via APIs, unlocking entirely new business models.
2.2 Diversified Development of Profit Mechanisms
The profit models of distributed AI computing power centers exhibit diversity and flexibility, with key revenue streams including:
1. Pay-as-you-go Model: The most direct profit method, where customers pay based on API call volume or computing resource consumption. Its advantage lies in on-demand pricing, allowing enterprises to dynamically adjust resources according to business cycles—reducing overall costs by 30–50%.
2. Revenue-Sharing Model: Common in decentralized platforms. For example, Inflectiv’s model allocates 70% of revenue to creators, 20% to curators improving data quality, and 10% to platform sustainability. This mechanism effectively incentivizes ecosystem participants.
3. Hybrid Pricing Model: Becoming mainstream, typically combining “annual subscriptions for basic functions + pay-as-you-go for premium services.” This model ensures stable base revenue while capturing premiums from high-value services.
Case in point: Together AI has demonstrated a strong growth trajectory—with ARR (Annual Recurring Revenue) of $50 million in 2023, $200 million in 2024, a projected $800 million in 2025, and a target of over $2 billion in 2026. It maintains an 80% gross margin and 25% net profit margin, driven by offering open-source AI services 90% cheaper than OpenAI.
2.3 In-depth Cost Structure Optimization
The cost structure of distributed AI computing power centers is characterized by operational costs accounting for 60–70% and initial investment for 30–40%. Within initial investment:
– Servers and hardware: 50–60%;
– Data center construction: 30–40%;
– Software and integration: 10–20%.
Operational costs are more complex, with breakdowns including:
– Power Costs: 50–60% of operational costs, with data center power consumption per unit computing power at approximately 0.5–1.0 kW/TOPS;
– Maintenance and Labor: 20–30%, with a 1,000-rack data center requiring 20–30 operation and maintenance staff, costing approximately $2–3 million annually in salaries;
– Network and Bandwidth: 10–20%, with annual costs of $100,000–$200,000 for 1Gbps bandwidth.
Notably, labor costs are often underestimated, accounting for 15–25% of a data center’s TCO (Total Cost of Ownership). This has driven the development of AIOps (AI-driven operations), which is expected to reduce data center O&M labor costs by 40% and shorten MTTR (Mean Time to Repair) by 75%.
In terms of economies of scale: Building 1GW of AI data center capacity costs approximately $35 billion (per Bernstein analysis), lower than NVIDIA’s own estimate of $50–60 billion. By optimizing site selection—choosing regions with surplus power and cooler climates—annual power costs can be reduced by $50–60 million, yielding $2.7 billion in cumulative savings over 30 years.
2.4 Business Model Innovation by Key Enterprises
Heavy-Asset Models of Tech Giants
– Microsoft: Invested $80 billion in Azure AI expansion;
– Amazon AWS: Expected to double self-developed ASIC shipments by 2025.
These enterprises have built strong scale advantages and technical barriers through large-scale infrastructure investment.
Light-Asset Models of Specialized Service Providers
– CoreWeave: Successfully transitioned from cryptocurrency mining to GPU cloud infrastructure, generating over $1 billion in revenue in 2024, with a pay-per-GPU-hour pricing model driving rapid growth;
– Crusoe Energy: Achieved $276 million in revenue in 2024 (up 82% YoY), with customer count increasing by over 7x—serving giants including Microsoft and Oracle;
– Replicate: Known as the “GitHub for AI models,” its 2025 revenue mix includes 60% usage-based (public models), 25% private deployment, and 15% enterprise contracts, with ARR of approximately $40 million.
These enterprises share common traits: leveraging technological innovation, business model optimization, or niche market breakthroughs to carve out a niche in a market dominated by giants.
III. Market Scale and Competitive Landscape: New Opportunities in a Global Trillion-Dollar Market
3.1 Explosive Growth of Global Market Scale
The distributed AI computing power center market is experiencing unprecedented growth. According to the latest data:
– The global AI computing power market exceeded $1.2 trillion in 2025 and is projected to reach $5.8 trillion by 2030, with a CAGR (Compound Annual Growth Rate) of 37.6%;
– China, accounting for 38% of the market, has become the world’s largest consumer of AI computing power.
In niche markets:
– The distributed edge AI market grew from $7.32 billion in 2024 to an estimated $8.64 billion in 2025, and is expected to reach $45 billion by 2035 (CAGR: 18%);
– The overall AI infrastructure market expanded from $46.45 billion in 2024 to $87.6 billion in 2025, with a projected $197.64 billion by 2030 (CAGR: 17.71%);
– The AI data center market is set to surge from $44 billion in 2025 to $933.76 billion by 2030 (CAGR: 31.6%).
These figures underscore the enormous potential of the distributed AI computing power center market.
3.2 Differentiated Development of Regional Markets
Global computing power investment shows a pattern of “rising in the East, declining in the West”:
– The Asia-Pacific region leads with an 18.2% growth rate, significantly outpacing North America (9.5%) and Europe (7.8%);
– This trend reflects the late-mover advantages and huge demand for AI infrastructure in emerging markets.
In terms of market share distribution (with slight variations across institutions), the overall landscape remains stable:
– Asia-Pacific: 33.5–35% (some reports indicate China accounts for 40% of the global market);
– North America: 30.3–36%;
– Western Europe: 20.2%;
– Other regions (Middle East, Africa, Latin America, etc.): 16.1%.
China’s market performance is particularly striking. According to the 2025 Assessment Report on China’s Artificial Intelligence Computing Power Development, Beijing, Hangzhou, and Shanghai maintained their top three positions in China’s 2024 AI City Ranking. By 2026, China’s AI server market is projected to reach $34.7 billion, accounting for 38% of the global share.
In application distribution, China’s intelligent computing power applications exhibit distinct characteristics: the Internet industry accounts for 53% of the market, followed by the service industry (18%), government sectors (9%), and the remaining 20% spread across finance, manufacturing, healthcare, and telecommunications.
3.3 Polarization Trend in the Competitive Landscape
The competitive landscape of the distributed AI computing power center market is shifting from oligopoly to polarization. Traditional cloud giants remain dominant, with AWS, Microsoft Azure, and Google Cloud collectively holding approximately 65–66% of the market share.
In the GPU market, NVIDIA’s dominant position remains unshakable—capturing 92% of the discrete GPU market in Q1 2025. However, large tech companies are accelerating self-developed chip initiatives to reduce dependence. JPMorgan predicts that by 2028, custom chips designed by Google, Amazon, Meta, OpenAI, and others will account for 45% of the AI chip market (up from an estimated 40% in 2025).
In the cloud computing market, the competitive landscape is relatively stable but with evolving shares:
– AWS: Approximately 29–31% of the cloud infrastructure market, leading in GPU cloud AI training;
– Microsoft Azure: Captured 22% of the global cloud market in Q1 2025, up from 21% in Q4 2024;
– Google Cloud: Smaller market share but growing rapidly.
3.4 The Rise of Emerging Players
Beyond traditional giants, a group of emerging specialized computing power service providers is growing rapidly:
– CoreWeave: Successfully transitioned from cryptocurrency mining to GPU cloud infrastructure, generating over $1 billion in revenue in 2024 and emerging as a standout in the AI infrastructure sector. Its successful transformation demonstrates the enormous opportunities in the distributed AI computing power market.
– Chinese Innovative Enterprises: PPIO Cloud, leveraging technological innovation in AI infrastructure, was selected for the “2025 China AI Industry Pioneers” list. With daily token calls exceeding 180 billion, it has become a representative enterprise in the AI infrastructure niche.
– Specialized Chip Vendors: In the network equipment sector, Juniper holds a leading 44% share in the 800GbE OEM switch market, serving as a key driver of AI data center infrastructure.
These emerging enterprises share common traits: leveraging technological innovation, business model innovation, or niche market breakthroughs to survive and grow in a giant-dominated market. Their rise not only enriches the market ecosystem but also drives technological progress and cost reduction across the industry.
IV. Policy Environment: Reshaping Rules for the Global AI Infrastructure Race
4.1 Strict Regulation of Data Security and Privacy Protection
Data security and privacy protection have become the top regulatory priorities for global AI infrastructure development. The EU’s GDPR (General Data Protection Regulation)—the world’s strictest data protection law—has exerted a profound impact on AI applications. Its core principle: any enterprise processing data of EU residents is subject to GDPR, regardless of whether the enterprise is located within the EU.
GDPR-permitted cross-border data transfer mechanisms include:
– Transfers via “certified codes of conduct” or “certification mechanisms”;
– Transfers via “Standard Contractual Clauses (SCCs)” or “Binding Corporate Rules (BCRs)”;
– Direct transfers to countries/regions deemed by the European Commission to have “adequate” data protection levels (e.g., the UK, Japan, Canada).
The United States has also adopted strict measures for data security. The U.S. Department of Commerce issued an interim final rule implementing a three-part strategy to regulate the proliferation of advanced AI models: imposing global licensing requirements for the export, re-export, or transfer (in-country) of advanced computing ICs or model weights of certain advanced AI models to any end-user in any destination.
The U.S. Department of Justice’s final rule—titled “Preventing Access by Covered Countries or Covered Persons to Sensitive Personal Data and Government-Related Data in the United States”—introduces significant restrictions and prohibitions on data transactions involving specific foreign countries.
4.2 Energy Consumption Control and Green Development Requirements
As demand for AI computing power grows explosively, energy consumption control has become a focus of governments worldwide. China has implemented strict measures through its dual control policy on energy consumption, restricting data center construction and requiring a PUE ≤ 1.3. This policy has directly driven the popularization of liquid cooling technology and the construction of green data centers.
To address energy challenges, governments globally are promoting the green transformation of data centers:
– Encouraging the use of renewable energy, requiring new data centers to be equipped with a certain proportion of clean energy;
– Promoting liquid cooling technology to reduce PUE to below 1.2;
– Supporting intelligent energy management systems to optimize energy efficiency through AI technology.
These policies are not only environmental requirements but also critical leverage in global AI competition. The ability to ensure computing power supply while achieving green development will determine competitive advantages in the future.
4.3 Government Support Policies and Financial Investment
Governments worldwide have identified AI infrastructure as a national strategic priority, investing heavily in its development:
China
The State Council issued Opinions on Further Implementing the “Artificial Intelligence +” Initiative, explicitly supporting breakthroughs in AI chip innovation and enabling software ecosystem development, as well as accelerating technological breakthroughs and project implementation of ultra-large-scale intelligent computing clusters. The document emphasizes increasing financial and fiscal support for AI, and developing long-term capital, patient capital, and strategic capital.
United States
The Biden administration issued an executive order directing the Department of Defense and the Department of Energy to coordinate with other federal agencies to lease federal sites to private-sector entities for the construction of large-scale AI data centers and related clean energy facilities. The Trump administration’s AI action plan focuses on building data centers and other new infrastructure, positioning “new energy” as the driver for “powering everything.”
United Kingdom
The government launched an ambitious computing roadmap, planning to invest over £1 billion to expand AI research resources (AIRR) by 20x by 2030, while investing £750 million in building a new national supercomputer in Edinburgh.
Hong Kong (China)
To promote the development of the local AI ecosystem, the government allocated HK$3 billion to launch a three-year AI funding program, supporting local institutions, R&D centers, government departments, and enterprises in leveraging the computing power of the Hong Kong Science and Technology Parks Corporation’s AI Supercomputing Centre (AISC) to achieve scientific breakthroughs.
4.4 International Cooperation and Standard-Setting
In the field of AI infrastructure, international cooperation is strengthening, but it also faces challenges such as technological blockades and standard competition:
China’s International Cooperation Initiatives
China released the Global AI Governance Initiative, emphasizing strengthening international cooperation on AI capacity building, and encouraging leading AI countries to take concrete actions—such as cooperating in developing AI infrastructure, establishing joint laboratories, building security assessment mutual recognition platforms, and organizing AI capacity-building training programs.
Technology Standard-Setting
Industry players are actively promoting the unification of technical standards. The UA Link 1.0 specification—expected to be released in Q1 2025—can connect up to 1,024 accelerators with a transmission speed of 200Gbps per channel. Enterprises including Apple, Alibaba, and Sinovac have joined the AI Infrastructure Standards Committee to jointly drive the development of industry standards.
Cross-Border Data Flow Rules
Countries worldwide are formulating their own cross-border data flow rules. Saudi Arabia is actively implementing its Personal Data Protection Law (PDPL), with the Saudi Data and Artificial Intelligence Authority (SDAIA) and the National Cybersecurity Authority (NCA) launching detailed implementation mechanisms for cross-border data transfers.
This complex policy environment presents both opportunities and challenges for distributed AI computing power centers. Enterprises must drive the healthy development of the industry through technological innovation and international cooperation while ensuring compliance.
V. Vertical Application Scenarios: Unlocking the Value of Distributed AI Computing Power
5.1 Healthcare: End-to-End Innovation from Diagnosis to R&D
The healthcare sector represents one of the most mature and widespread application scenarios for distributed AI computing power. AI computing power networks in healthcare function like “shared computing power banks,” connecting distributed servers, GPUs, and other computing resources into a network for dynamic scheduling based on demand. For example, when a hospital needs to analyze 1,000 CT scans, the system automatically allocates idle GPU resources from nearby locations.
In medical image analysis, AI technology demonstrates enormous potential:
– Google released Med Gemma—its most powerful open model for multimodal medical text and image understanding—with the potential to accelerate the development of new medical products;
– Commercial AI tools such as Zebra Medical Vision and Aidoc enable faster, more reliable detection of abnormalities in X-rays, CT scans, and MRIs;
– Predictive diagnostics can assess patient scans to predict disease progression, enabling early intervention.
Drug R&D is another key application scenario:
– Novo Nordisk partnered with NVIDIA to leverage DCAI’s Gefion supercomputer (powered by NVIDIA DGX SuperPOD) to provide an AI factory for Novo Nordisk’s drug discovery and intelligent agent AI workloads;
– Novo Nordisk will use NVIDIA BioNeMo for generative AI-driven drug discovery, and NVIDIA NIM and NeMo microservices to build customized intelligent agent workflows.
The application of edge computing in healthcare is transforming traditional medical models:
– A 2025 Nature Medicine study showed that a hybrid architecture combining edge computing and federated learning reduced misdiagnosis rates in primary healthcare institutions by 27%;
– By integrating the powerful reasoning capabilities of large language models, the privacy protection mechanisms of federated learning, and the real-time advantages of edge computing, patients in remote areas can now access top-tier medical expertise.
AMD’s machine learning inference technology enables early detection of key diseases by identifying abnormalities in X-rays, ultrasounds, digital pathology, dermatology, and ophthalmology. Other applications include surgical tool guidance, drug discovery, and genomic analysis.
5.2 Autonomous Driving: An Intelligent Revolution in Vehicle-Road Collaboration
The autonomous driving sector has the most stringent requirements for distributed AI computing power, demanding millisecond-level decision-making speed and ultra-high reliability.
The CoEdge system represents the latest development in collaborative edge computing systems for autonomous driving. This distributed computing paradigm pushes services from the cloud to the network edge to support IoT applications such as intelligent transportation and autonomous driving. CoEdge enables real-time detection before a vehicle enters a detection zone, reducing storage and computing burdens.
In onboard computing platforms:
– The NVIDIA DRIVE AGX Hyperion is a comprehensive autonomous driving platform integrating a complete sensor architecture, high-performance AI computing, and a robust software stack to provide AI computing power for safe, intelligent driving;
– The NVIDIA DriveOS safety-certified operating system enables real-time processing, safety, and system monitoring to meet functional safety requirements.
Edge AI plays a critical role in autonomous driving: in autonomous vehicles, the “edge” refers to onboard System-on-Chips (SoCs) that process inputs from cameras, LiDAR, radar, GPS, and other sensors to enable faster real-time decision-making on the road.
5G/6G networks are critical infrastructure for intelligent driving: 5G already provides sufficient bandwidth and low latency to support real-time communication between vehicles, the cloud, other vehicles, and roadside infrastructure. Future 6G development will further enhance communication performance.
Computing power requirements for autonomous driving cover multiple areas:
– Real-time sensor fusion (LiDAR, radar, and cameras);
– Vision-language modeling for scene understanding;
– Autonomous driving perception, planning, and control inference;
– Passenger interaction, driver monitoring, and infotainment.
These requirements are driving the rapid development of onboard edge computing platforms—from high-performance GPU-driven systems to energy-efficient NVIDIA Jetson platforms, designed for robotics, off-road vehicle ADAS, and AI-driven production line inspection.
5.3 Finance: A New Paradigm for Risk Management and Intelligent Decision-Making
Demand for distributed AI computing power in finance focuses on three key areas: risk management, trading algorithms, and customer service.
The collaboration between the London Stock Exchange Group (LSEG) and Databricks demonstrates AI’s application in financial risk management: unifying and enhancing market, credit, and counterparty risk oversight through AI-driven monitoring, risk exposure tracking, and real-time compliance from front to back office.
In trading algorithms, latest research integrates improved Group Relative Policy Optimization (GRPO) algorithms with financial news risk and sentiment signals based on large language models to develop new trading agents. These systems process massive data streams in real time, identify profitable opportunities, and dynamically adjust positions—improving efficiency and reducing transaction costs.
The practice of Bank of Beijing demonstrates how financial institutions build distributed AI infrastructure:
– Centered on the “Jingzhi Brain” as the core node, with its credit card center and branches as intelligent computing nodes;
– Leveraging AI + IoT + edge computing to build a bank-wide intelligent computing network with “cloud-network-edge-end” collaboration;
– Extending the intelligent middle platform capabilities of the headquarters to various business units and branch outlets to provide low-latency, high-concurrency AI inference and data analysis application capabilities.
Edge AI applications in financial services cover multiple areas:
– Fraud detection;
– Automated trading;
– Personalized banking experiences;
– Risk management.
These applications enable financial institutions to deliver more responsive, efficient services while ensuring data security and compliance. For example, HSBC has deployed Pepper robots in some branches, which use natural language processing to interact with customers and can even detect basic emotions.
5.4 Market Demand Characteristics and Development Trends
Analysis of the three vertical sectors reveals common demand characteristics of distributed AI computing power in vertical applications:
1. Extremely High Real-Time Requirements: Healthcare diagnostics require rapid results, autonomous driving needs millisecond-level decisions, and financial transactions demand real-time responses—driving deep integration of edge computing and distributed AI.
2. Strong Demand for Data Privacy Protection: Healthcare data involves personal health privacy, financial data relates to asset security, and autonomous driving data includes travel trajectories. Privacy-preserving technologies such as federated learning and homomorphic encryption have become critical enablers.
3. Strict Reliability and Security Requirements: All three sectors involve life safety or property security; any system failure could cause severe consequences. This requires distributed AI systems to have extremely high reliability and comprehensive security mechanisms.
From a development trend perspective:
– Edge computing will take on more local processing tasks to reduce data transmission latency;
– The cloud-edge-end collaborative architecture will become mainstream, fully leveraging the advantages of each layer;
– Industry-specific AI models will continue to emerge, improving application efficiency;
– Privacy-preserving computing technologies will develop rapidly, balancing data utilization and privacy protection.
These trends will drive distributed AI computing power centers toward greater specialization, intelligence, and security—providing strong support for the digital transformation of various vertical sectors.
Strategic Recommendations and Outlook
Recommendations for Technology Development Paths
Based on analysis of technological evolution trends, enterprises are advised to adopt a “progressive innovation” strategy:
– Short-Term (1–2 years): Focus on popularizing liquid cooling technology and optimizing heterogeneous computing. Prioritize deploying cold plate liquid cooling solutions to reduce PUE to below 1.2, while gradually introducing multiple accelerators (GPU, NPU, ASIC) to build initial heterogeneous computing capabilities.
– Mid-Term (3–5 years): Focus on edge computing and the cloud-edge-end collaborative architecture. As 75% of enterprise data will be processed at the edge by 2025, enterprises need to build distributed computing power networks to enable dynamic scheduling and optimal configuration of computing resources.
– Long-Term (5+ years): Layout cutting-edge fields such as optical computing and quantum computing. Meanwhile, closely monitor the development of disruptive technologies like Model Mesh Networks and advance technical reserves and strategic planning.
Directions for Business Model Innovation
In terms of business models, enterprises are advised to explore a “platformization + ecosystem” development path:
1. Build Open Platforms: Learn from Together AI’s success by quickly capturing market share through services 90% cheaper than traditional solutions. Adopt hybrid pricing models to subscriptionize basic services and commercialize value-added services.
2. Develop Ecosystem Cooperation: Draw on Inflectiv’s revenue-sharing model to establish reasonable benefit distribution mechanisms, attracting more developers and users to participate in ecosystem development. Particularly in vertical sectors, build strategic partnerships with industry leaders.
3. Innovate Profit Models: Beyond traditional computing power leasing, actively explore new models such as AI-as-a-Service (AIaaS), Model-as-a-Service (MaaS), and Data-as-a-Service (DaaS) to build a diversified revenue structure.
Market Competition Strategies
Facing intense market competition, enterprises should adopt a “differentiation + specialization” competitive strategy:
1. Focus on Vertical Sectors: Avoid direct competition with giants in the general market; instead, deepen engagement in vertical sectors such as healthcare, autonomous driving, and finance, and build competitive advantages through specialized services.
2. Achieve Technological Innovation Breakthroughs: Drive breakthroughs in specific technical areas (e.g., edge computing optimization, privacy-preserving computing, energy management) to gain market recognition through technological leadership.
3. Deepen Regional Market Penetration: Particularly in high-growth markets such as Asia-Pacific, build regional competitive advantages through localized services and in-depth cooperation.
Policy Compliance Recommendations
In a complex policy environment, enterprises need to establish a “proactive compliance + flexible response” strategy:
1. Build Compliance Systems: Develop comprehensive data governance systems to ensure compliance with cross-border data flow regulations (e.g., GDPR, CCPA, PIPL) across different countries and regions.
2. Monitor Policy Trends: Closely track national AI development strategies and related policies—particularly government funding support programs and technical standard-setting—to adjust business strategies in a timely manner.
3. Strengthen International Cooperation: Actively participate in international standard-setting and industry organization activities to address technological blockades and trade barriers through multilateral cooperation.
Future Outlook
The distributed AI computing power center industry is on the cusp of explosive growth. Technological innovation is advancing rapidly—from liquid cooling to heterogeneous computing, and from edge computing to cloud-edge collaboration—with each breakthrough reshaping the industry landscape. Business models are constantly evolving—from traditional computing power leasing to platform-based operations, and from centralization to distribution—unlocking new opportunities.
Market demand is surging: the global AI computing power market exceeded $1.2 trillion in 2025, with a CAGR of 37.6%. China, accounting for 38% of the market, has become the world’s largest consumer, while the Asia-Pacific region leads with an 18.2% growth rate—far outpacing North America and Europe. This growth stems not only from technological progress but also from the deep integration of AI across industries.
While the policy environment is complex, it is generally supportive and encouraging. Governments worldwide have identified AI infrastructure as a national strategy, investing heavily in its development. At the same time, growing attention to data security and privacy protection is driving the industry toward greater standardization and sustainability.
Looking ahead, the distributed AI computing power center industry will exhibit the following trends:
1. Accelerated Technology Integration: AI, cloud computing, edge computing, 5G/6G, and IoT will deeply integrate to form a more intelligent, efficient infrastructure system.
2. Expanded Application Scenarios: Penetration will extend from current sectors (healthcare, autonomous driving, finance) to manufacturing, retail, education, energy, and other industries—enabling full-scale intelligent transformation.
3. Improved Industrial Ecosystem: A complete industrial chain will form, covering chips, servers, network equipment, software platforms, and application services—unleashing full industrial synergy effects.
4. Green Development as Inevitable: As energy consumption becomes an increasingly pressing issue, green, low-carbon, and sustainable development will become a key industry direction. Technologies such as liquid cooling, renewable energy, and intelligent energy management will be widely adopted.
For industry participants, this is an era of immense opportunity. Through technological innovation, model innovation, and ecosystem cooperation, enterprises can carve out a niche in this trillion-dollar market. The key is to maintain strategic focus, adhere to long-termism, seize opportunities amid change, and achieve breakthroughs through innovation.
Distributed AI computing power centers are not only a product of technological progress but also a crystallization of human wisdom and creativity. They will drive human society into a new intelligent era—an era we can all look forward to and participate in shaping.
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