PUE = 总设施能耗 / IT设备能耗

┌─────────────────────────────────┐
│    应用层 (Application)         │
├─────────────────────────────────┤
│    操作系统 (OS)                │
├─────────────────────────────────┤
│    虚拟化层 (Hypervisor)        │
├─────────────────────────────────┤
│    物理硬件 (CPU, Memory, I/O)  │
└─────────────────────────────────┘

┌─────────────┐
│  互联网      │
└──────┬──────┘
       │
┌──────▼──────┐
│  边界路由器  │
└──────┬──────┘
       │
┌──────▼──────────────────┐
│     核心交换机          │
└──────┬──────────────────┘
       │
┌──────▼──────┐  ┌──────▼──────┐
│  汇聚交换机  │  │  汇聚交换机  │
└──────┬──────┘  └──────┬──────┘
       │                │
┌──────▼──────┐  ┌──────▼──────┐
│  接入交换机  │  │  接入交换机  │
└──────┬──────┘  └──────┬──────┘
       │                │
┌──────▼──────┐  ┌──────▼──────┐
│   服务器     │  │   服务器     │
└─────────────┘  └─────────────┘

模型副本1 ──┐
模型副本2 ──┤
模型副本3 ──┼──> 梯度聚合 ──> 参数更新
模型副本4 ──┤
模型副本N ──┘

Layer 1 ──> Layer 2 ──> Layer 3 ──> ... ──> Layer N
  GPU1       GPU2        GPU3                 GPUN

GPU1: Layer 1-10
GPU2: Layer 11-20
GPU3: Layer 21-30
...

服务器机架
    │
    ├── 冷却液分配单元（CDU）
    │       │
    │       ├── 泵送系统
    │       ├── 热交换器
    │       └── 控制系统
    │
    └── 外部冷却系统
            │
            ├── 干式冷却器（空气冷却）
            ├── 冷却塔（水冷却）
            └── 地热系统（自然冷却）

数据中心能源系统
    │
    ├── 风电（40-50%）
    ├── 光伏（20-30%）
    ├── 地热（20-30%）
    ├── 储能系统（10-20%）
    └── 电网备用（<10%）

云端数据中心 ──┐
                ├──> 协同计算
边缘节点 ──────┘

Data Centre Research: Computing Infrastructure in the AGI Era

Exploring how modern data centres support the computational needs of the AGI era

📋 Table of Contents

1. Introduction
2. Data Centre Overview
3. Core Technical Architecture
4. Energy Efficiency Optimization
5. AI-Accelerated Computing
6. Hyperscale Data Centres
7. Liquid Cooling Revolution
8. Renewable Energy and Circular Data Centres
9. Data Centres and AGI
10. Future Development Trends
11. Conclusion

---

Introduction

In today's rapidly evolving artificial intelligence landscape, particularly with the rise of large language models (LLMs) and artificial general intelligence (AGI), data centres, as the core of computing infrastructure, are facing unprecedented challenges and opportunities. From ChatGPT to GPT-4, from training to inference, every AI milestone relies on powerful data centre support.

This article will provide an in-depth technical exploration of modern data centre key technologies, architectural evolution, energy efficiency optimization, and how they provide the computational foundation for the AGI era.

---

Data Centre Overview

What is a Data Centre?

A data centre is a physical facility that centralizes the storage, management, and processing of large amounts of data, including:

• Server Clusters: Core hardware for executing computing tasks
• Storage Systems: Data persistence and backup
• Network Equipment: Internal and external communication
• Cooling Systems: Maintaining equipment at optimal temperatures
• Power Systems: Uninterruptible power supply (UPS) and backup generators
• Security Systems: Physical and network security protection

Data Centre Classification

1. By Scale

• Enterprise Data Centres: Serve a single organization, smaller scale
• Colocation Data Centres: Provide infrastructure services to multiple clients
• Hyperscale Data Centres: Operated by cloud service providers, massive scale (typically over 100,000 servers)

2. By Service Model

• IaaS (Infrastructure as a Service): Provides computing, storage, and network resources
• PaaS (Platform as a Service): Provides development and deployment platforms
• SaaS (Software as a Service): Provides application software services

Key Data Centre Metrics

PUE (Power Usage Effectiveness)

PUE = Total Facility Energy / IT Equipment Energy

• Ideal Value: Close to 1.0 (all power used for IT equipment)
• Industry Average: Approximately 1.5-2.0
• Excellent Level: 1.1-1.3
• Hyperscale Data Centres: Can achieve 1.05-1.1

Availability Tier

• Tier I: 99.671% availability (28.8 hours/year downtime)
• Tier II: 99.741% availability (22.7 hours/year)
• Tier III: 99.982% availability (1.6 hours/year)
• Tier IV: 99.995% availability (0.4 hours/year)

---

Core Technical Architecture

1. Server Architecture Evolution

Traditional Server Architecture

┌─────────────────────────────────┐
│    Application Layer            │
├─────────────────────────────────┤
│    Operating System (OS)        │
├─────────────────────────────────┤
│    Virtualization Layer         │
│    (Hypervisor)                 │
├─────────────────────────────────┤
│    Physical Hardware            │
│    (CPU, Memory, I/O)          │
└─────────────────────────────────┘

Modern Cloud-Native Architecture

• Containerization: Docker, Kubernetes
• Microservices: Service decoupling, independent scaling
• Serverless: On-demand computing, auto-scaling
• Edge Computing: Reduced latency, improved responsiveness

2. Storage Architecture

Storage Hierarchy

1. L1 Cache: Inside CPU, nanosecond access
2. L2/L3 Cache: Inside CPU, nanosecond access
3. HBM (High Bandwidth Memory): GPU-specific, TB/s bandwidth, microsecond access
4. Memory (DDR5): GB to TB capacity, microsecond access
5. NVMe SSD (PCIe 5.0): TB capacity, microsecond access, read/write speeds up to 14GB/s
6. 3D NAND SSD: Multi-layer stacking, TB to PB capacity, millisecond access
7. SATA SSD: TB capacity, millisecond access
8. HDD: PB capacity, millisecond access
9. Tape Storage: Archive storage, second-level access

HBM (High Bandwidth Memory) Technology

HBM Development Timeline

• HBM1: 2015, 1GB capacity, 128GB/s bandwidth
• HBM2: 2016, 8GB capacity, 256GB/s bandwidth
• HBM2e: 2018, 16GB capacity, 460GB/s bandwidth
• HBM3: 2022, 24GB capacity, 819GB/s bandwidth (per stack)
• HBM3e: Released 2024, 36GB capacity, 1.2TB/s bandwidth (per stack)
• HBM4: Expected 2026 release, further capacity and bandwidth improvements, supporting higher-performance AI workloads

HBM Technology Features

• 3D Stacking: Multiple DRAM layers vertically stacked
• TSV (Through-Silicon Via): Enables vertical interconnects
• High Bandwidth: 10-20x bandwidth improvement over DDR5
• Low Power: Lower power consumption per bandwidth unit
• Small Form Factor: Saves PCB space

HBM Applications in AI Data Centres

• GPU Memory: NVIDIA H100 (80GB HBM3), AMD MI300X (192GB HBM3)
• Training Acceleration: Reduces memory access bottlenecks, improves training efficiency
• Inference Optimization: Enables larger models to run on single cards

Distributed Storage Systems

• Object Storage: Amazon S3, Azure Blob Storage
• Block Storage: Amazon EBS, Azure Disk
• File Storage: NFS, CIFS, distributed file systems

3. Network Architecture

Network Topology

┌─────────────┐
│  Internet   │
└──────┬──────┘
       │
┌──────▼──────┐
│ Edge Router │
└──────┬──────┘
       │
┌──────▼──────────────────┐
│   Core Switch           │
└──────┬──────────────────┘
       │
┌──────▼──────┐  ┌──────▼──────┐
│ Aggregation │  │ Aggregation │
│   Switch    │  │   Switch    │
└──────┬──────┘  └──────┬──────┘
       │                │
┌──────▼──────┐  ┌──────▼──────┐
│ Access      │  │ Access      │
│   Switch    │  │   Switch    │
└──────┬──────┘  └──────┬──────┘
       │                │
┌──────▼──────┐  ┌──────▼──────┐
│  Servers    │  │  Servers    │
└─────────────┘  └─────────────┘

Network Technology Evolution

• Ethernet Evolution: 10GbE → 25GbE → 100GbE → 400GbE → 800GbE (commercially available 2024, expected mainstream by 2026)
• InfiniBand: Mainstream choice for HPC and AI training clusters, supporting 200Gb/s, 400Gb/s
• RDMA (Remote Direct Memory Access): Reduces latency to microsecond level, improves bandwidth utilization
• SDN (Software-Defined Networking): Centralized control, flexible configuration
• NFV (Network Functions Virtualization): Network functions softwareization
• AI Data Centre Networks: Network architectures optimized for AI workloads, supporting large-scale GPU cluster communication

AI Data Centre Network Architecture (2024-2026)

Juniper AI Data Centre Network Solutions

• Ultra-Low Latency Switching: Supports nanosecond-level latency
• High-Bandwidth Interconnect: Supports 800GbE port density
• Intelligent Traffic Scheduling: AI-driven network optimization
• Scalable Architecture: Supports tens of thousands of GPU node interconnects

Network Topology Optimization

• Clos Network: Multi-stage switching, supports large-scale expansion
• Dragonfly Topology: Reduces hop count, lowers latency
• All-Optical Interconnect: Optical switching technology, improves bandwidth and reduces power consumption

---

Energy Efficiency Optimization

1. Cooling System Optimization

Traditional Air Cooling

• CRAC (Computer Room Air Conditioning): Forced air circulation
• Hot Aisle/Cold Aisle Layout: Prevent hot air recirculation
• Issues: High energy consumption, limited cooling efficiency

Liquid Cooling Technology

Direct Liquid Cooling

• Cooling liquid directly contacts heat-generating components
• Cooling efficiency 1000x higher than air cooling
• Supports higher power density

Immersion Cooling

• Servers completely immersed in cooling liquid
• Eliminates fan requirements
• PUE can be reduced to 1.02-1.05

Cold Plate Cooling

• Cooling plate contacts CPU/GPU
• Some components still use air cooling
• Balances cost and effectiveness

2. Power Management

High-Efficiency Power Modules

• 80 PLUS Certification: Power efficiency standards
  • 80 PLUS: 80% efficiency (20% load)
  • 80 PLUS Titanium: 96% efficiency (50% load)
• Modular UPS: Scale on demand, improve efficiency
• HVDC (High Voltage Direct Current): Reduce conversion losses

Intelligent Power Management

• DVFS (Dynamic Voltage and Frequency Scaling): Adjust based on load
• Server Hibernation: Auto-hibernate at low load
• Load Balancing: Optimize workload distribution

3. Virtualization and Resource Optimization

Server Virtualization

• CPU Virtualization: Intel VT-x, AMD-V
• Memory Virtualization: Memory overcommitment, memory ballooning
• I/O Virtualization: SR-IOV, device passthrough

Container Optimization

• Resource Limits: CPU, memory quotas
• Auto-scaling: Kubernetes HPA/VPA
• Multi-tenant Isolation: Namespaces, resource quotas

---

AI-Accelerated Computing

1. AI-Specific Hardware

GPU (Graphics Processing Unit)

NVIDIA GPU Series

• A100: Released 2020, 80GB HBM2e, 2TB/s memory bandwidth, 624 TFLOPS (FP16)
• H100: Released 2022, 80GB HBM3, 3TB/s memory bandwidth
  • Transformer Engine: Automatic mixed FP8/FP16 precision, 9x training speedup
  • NVLink 4.0: 900GB/s GPU-to-GPU interconnect bandwidth
  • DPX Instructions: Accelerates dynamic programming algorithms
• H200: Released 2024, 141GB HBM3e, 4.8TB/s memory bandwidth
• B100: Released 2024, Blackwell architecture, significant performance improvements over H100
• A800/H800: China-specific versions, compliant with export control requirements

AMD GPU Series

• MI250X: CDNA2 architecture, 128GB HBM2e, 47.9 TFLOPS (FP64)
• MI300X: Released 2024, CDNA3 architecture, 192GB HBM3, 5.2TB/s memory bandwidth
  • CDNA3 Architecture Features:
    • Chiplet design, improves yield and performance
    • Supports FP64, FP32, FP16, BF16, INT8 multiple precisions
    • Optimized Matrix Cores
    • Enhanced Infinity Fabric interconnect technology
• MI350X: Expected 2026 release, next-generation CDNA architecture, further performance improvements

TPU (Tensor Processing Unit)

• Google TPU v4: Optimized for machine learning
• TPU v5: Higher performance, supports larger models

Dedicated AI Chips

• Cerebras Wafer-Scale Engine: Wafer-sized chip, supports ultra-large model training
• Graphcore IPU: Intelligence Processing Unit, optimized for AI workloads
• SambaNova: Reconfigurable dataflow architecture, flexible AI acceleration
• Groq: LPU (Language Processing Unit), optimized for LLM inference
• Tenstorrent: Scalable AI chip architecture

In-Memory Computing

Technology Principles

• Compute-Storage Fusion: Execute computations directly in memory, reduce data movement
• Lower Latency: Eliminate CPU-memory data transfer bottlenecks
• Improve Energy Efficiency: Reduce power consumption from data movement

Application Scenarios

• Deep Learning Acceleration: Matrix operations executed in memory
• Graph Computing: Graph traversal and computation optimization
• Data Analytics: Real-time data analytics acceleration

Technical Challenges

• Precision Issues: Analog computing precision limitations
• Programmability: Requires new programming models
• Cost: Higher cost for specialized hardware

2. AI Training Infrastructure

Large-Scale Distributed Training

Data Parallelism

Model Replica 1 ──┐
Model Replica 2 ──┤
Model Replica 3 ──┼──> Gradient Aggregation ──> Parameter Update
Model Replica 4 ──┤
Model Replica N ──┘

Model Parallelism

Layer 1 ──> Layer 2 ──> Layer 3 ──> ... ──> Layer N
  GPU1       GPU2        GPU3                 GPUN

Pipeline Parallelism

GPU1: Layer 1-10
GPU2: Layer 11-20
GPU3: Layer 21-30
...

Training Optimization Techniques

• Mixed Precision Training: FP16/BF16, reduce memory and computation
• Gradient Accumulation: Simulate larger batches
• Checkpointing: Periodic saves, support resume training
• ZeRO Optimizer: Sharded optimizer states

3. AI Inference Optimization

Model Compression

• Quantization: INT8/INT4, reduce model size
• Pruning: Remove unimportant weights
• Knowledge Distillation: Small models learn from large models

Inference Acceleration

• TensorRT: NVIDIA inference optimization
• ONNX Runtime: Cross-platform inference
• Model Serving: TensorFlow Serving, Triton

---

Hyperscale Data Centres

1. Hyperscale Data Centre Characteristics

Scale Metrics (2024-2026 Data)

• Server Count: 100,000 - 1,000,000 servers, some hyperscale data centres exceed 1 million servers
• Floor Area: Tens of thousands to hundreds of thousands of square meters
• Power Capacity: Tens to hundreds of megawatts, largest data centres can reach hundreds of megawatts
• Investment Scale: Billions to tens of billions of dollars
• Global Data Centre Market Size: Exceeded 300 billion RMB in 2024 (China market), expected to exceed 450 billion RMB by 2026

Market Growth Trends

• Global HPC Market: Expected to grow from $1.9 billion in 2023 to $3.87 billion in 2031, CAGR 10.7%
• China Intelligent Computing: Reached approximately 600 EFLOPS in 2024, expected to exceed 1200 EFLOPS by 2026
• China Data Centre Racks: Reached approximately 8.7 million racks in 2024, expected to exceed 12 million racks by 2026
• China Intelligent Computing Centre IT Load: Expected to exceed 3000MW by 2026, CAGR 36%

Major Operators

• Amazon Web Services (AWS): World's largest cloud service provider
• Microsoft Azure: Enterprise cloud services
• Google Cloud Platform (GCP): AI and data analytics
• Alibaba Cloud: China's largest cloud service provider
• Tencent Cloud: Gaming and social cloud services

2. Hyperscale Data Centre Architecture

AWS HPC Architecture Best Practices (Well-Architected Framework)

Compute Optimization

• Instance Selection: Instance types optimized for HPC workloads
• Elastic Scaling: Dynamically adjust compute resources based on workload
• Spot Instances: Leverage Spot instances to reduce costs

Network Optimization

• Low Latency Network: Dedicated network paths, reduce latency
• High Bandwidth Interconnect: Support 400GbE and 800GbE
• Network Topology: Optimize network topology, reduce hop count

Storage Optimization

• Parallel File Systems: High-performance file systems like Lustre, GPFS
• Object Storage: Object storage like S3, support large-scale data
• Data Tiering: Tiered storage for hot, warm, and cold data

Reliability Design

• Redundant Architecture: N+1 or N+2 redundant design
• Fault Isolation: Fault domain isolation, prevent cascading failures
• Automatic Recovery: Automatic fault detection and recovery mechanisms

Modular Design

• Prefabricated Modules: Factory prefabricated, on-site assembly
• Standardized Components: Reduce costs, improve efficiency
• Rapid Deployment: Shorten from design to operation to months
• Scalability: Modular expansion, support gradual growth

Automated Operations

• Robotic Inspection: Automatically detect equipment status
• AI Predictive Maintenance: Early fault detection
• Automated Fault Recovery: Reduce manual intervention
• Intelligent Resource Scheduling: AI-driven resource optimization allocation

Data Centre Redundancy and Reliability

Redundancy Levels

• N Configuration: No redundancy, single point of failure causes service interruption
• N+1 Configuration: One backup component, can tolerate single point of failure
• N+2 Configuration: Two backup components, higher reliability
• 2N Configuration: Full redundancy, each component has backup

Reliability Metrics

• MTBF (Mean Time Between Failures): Equipment reliability metric
• MTTR (Mean Time To Repair): Fault recovery speed
• Availability Target: 99.99% (Tier IV) or higher

3. Geographic Distribution Strategy

Multi-Region Deployment

• Availability Zone (AZ): Independent data centres within the same region
• Region: Geographically separated data centre clusters
• Edge Nodes: Small data centres close to users

Data Localization

• Compliance Requirements: Meet data protection regulations
• Latency Optimization: Serve users nearby
• Disaster Recovery: Cross-region backup

---

Liquid Cooling Revolution

1. Why Liquid Cooling?

Power Density Challenges

• Traditional Air Cooling Limit: Approximately 15-20 kW per rack
• AI Server Requirements: 30-50 kW per rack, or higher
• Future Requirements: May reach 100+ kW per rack

Energy Efficiency Advantages

• PUE Reduction: From 1.5 to 1.05-1.1
• Cooling Energy Savings: Can save 30-50% cooling energy
• Space Utilization: Higher power density, reduce floor space

2. Liquid Cooling Technology Types

Single-Phase Immersion Cooling

• Coolant: Non-conductive engineering fluid (e.g., 3M Novec)
• Operating Temperature: 40-50°C
• Advantages: Simple and reliable, easy maintenance
• Applications: GPU servers, AI training clusters

Two-Phase Immersion Cooling

• Coolant: Low boiling point liquid (e.g., R-134a)
• Working Principle: Liquid boiling removes heat
• Advantages: Higher cooling efficiency
• Challenges: Complex system, requires sealing

Direct Chip Cooling

• Cold Plate Contact: CPU/GPU directly contacts cooling plate
• Coolant Circulation: Removes heat
• Applications: High-performance computing, AI training

3. Liquid Cooling System Architecture

Server Rack
    │
    ├── Cooling Distribution Unit (CDU)
    │       │
    │       ├── Pumping System
    │       ├── Heat Exchanger
    │       └── Control System
    │
    └── External Cooling System
            │
            ├── Dry Cooler (Air Cooling)
            ├── Cooling Tower (Water Cooling)
            └── Geothermal System (Natural Cooling)

4. Liquid Cooling Application Cases

Large Tech Companies

• Microsoft: Deploying liquid cooling in Azure data centres, supporting H100 clusters, PUE reduced to 1.05
• Google: TPU clusters use liquid cooling, PUE reduced to 1.06
• Meta: AI training clusters adopt immersion cooling, supporting MI300X clusters
• Alibaba: Self-developed liquid cooling technology, PUE reduced to 1.09, supporting large-scale AI training
• AWS: Deploying liquid cooling technology in HPC workloads

Sandia Labs Cooling Technology Research

Holistic Data Center Design

• System-Level Optimization: End-to-end optimization from chips to cooling systems
• Thermal Management Innovation: Hybrid solutions combining liquid and air cooling
• Energy Efficiency Improvement: PUE can be reduced to 1.02-1.03 through optimized cooling systems
• Cost Optimization: Balance initial investment and operational costs
• Sustainability: Reduce water consumption, improve cooling efficiency

Industry Trends (2024-2026)

• 2024: Rapid growth in liquid cooling market, widespread adoption in AI data centres, PUE reduced to 1.05-1.09
• 2025: Liquid cooling expected to account for 40%+ of AI data centres
• 2026 Forecast: 800GbE networks and liquid cooling become standard for AI data centres, liquid cooling expected to reach 60%+
• 2027-2030 Forecast: Liquid cooling may become standard configuration for all high-performance data centres, PUE expected to reach 1.02-1.03

---

Renewable Energy and Circular Data Centres

1. Data Centre Energy Challenges

Energy Demand Growth

• Global Data Centre Energy Consumption: Approximately 200 TWh/year in 2024
• 2030 Forecast: Expected to reach 300 TWh/year
• Energy Cost Proportion: 30-50% of operational costs
• Carbon Emission Pressure: Data centres account for approximately 1-2% of global ICT industry carbon emissions

Sustainable Development Goals

• Carbon Neutrality Targets: Major cloud service providers commit to achieving carbon neutrality by 2030-2040
• Renewable Energy Targets: Achieve 80%+ renewable energy supply by 2026, target 100% by 2030
• Energy Efficiency Improvement: Continuously reduce PUE and carbon emissions through technological optimization

2. Renewable Energy Applications in Data Centres

Wind Power

Technical Characteristics

• Stability: Suitable for large-scale centralized deployment
• Cost Advantage: Wind power costs continue to decline, approaching or below traditional energy
• Geographic Distribution: Suitable for building data centres in wind-rich regions

Application Cases

• Inner Mongolia Helingeer Cluster: Hyperscale data centres (37.5MW+ power) directly powered by wind power projects
  • Annual electricity consumption: hundreds of millions of kWh
  • Matched with centralized wind power bases
  • Reduce energy costs and increase green electricity consumption ratio
• Google: Deploys wind power projects in multiple data centres, achieving 100% renewable energy supply
• Microsoft: Builds data centres in wind-rich regions such as Ireland and the Netherlands

Technical Advantages

• Direct Power Supply: Reduce grid transmission losses
• Cost Optimization: Long-term power purchase agreements (PPA) lock in electricity prices
• Carbon Emission Reduction: Zero-carbon electricity supply

Solar PV

Technical Characteristics

• Distributed Deployment: Can be deployed on data centre roofs and surrounding areas
• Flexibility: Modular design, easy to expand
• Daytime Matching: Matches data centre high-load periods during the day

Application Cases

• Amazon: Deploys large-scale solar PV projects in multiple data centres worldwide
  • Single project capacity can reach hundreds of megawatts
  • Combined with energy storage systems for 24-hour clean energy supply
• Alibaba: Deploys solar PV + energy storage systems in Zhangbei data centre
• Tencent: Builds solar PV facilities in Guizhou data centre

Technical Advantages

• Local Consumption: Reduce transmission losses
• Energy Storage Integration: Combined with battery storage to smooth power supply
• Dual Utilization: Solar panels can provide shading and reduce building energy consumption

Geothermal Energy

Technical Characteristics

• Stability: 24-hour stable power supply, unaffected by weather
• Sustainability: Geothermal resources can be sustainably utilized
• High Capacity Factor: Can reach 90%+, far higher than wind and solar

Application Cases

• Meta and Sage Geosystems: Signed 150MW data centre power supply agreement
  • Utilizes Enhanced Geothermal Systems (EGS)
  • Stable and reliable clean energy supply
• Microsoft and KenGen: Builds geothermal-powered data centre in Kenya
  • Utilizes rich geothermal resources in the East African Rift Valley
  • Provides stable power for local data centres
• Google and Fervo Energy: Project Red expanded to 115MW
  • Primarily powers Google data centres
  • Uses advanced geothermal power generation technology

Technical Advantages

• Stability: Unaffected by weather, 24-hour stable power supply
• High Capacity Factor: Far higher than intermittent renewable energy
• Long-term Stability: Geothermal resources can be sustainably utilized for decades

Other Renewable Energy Sources

Hydropower

• Large Hydropower Stations: Provide stable power for hyperscale data centres
• Small Hydropower: Suitable for data centres in remote areas
• Pumped Storage: Combined with other renewable energy sources, provides peak-shaving capability

Biomass Energy

• Biogas Power Generation: Utilizes organic waste for power generation
• Application Scenarios: Suitable for data centres in agricultural regions

Marine Energy

• Tidal Energy: Applications in coastal data centres
• Wave Energy: Emerging technology, in pilot stage

3. Renewable Energy Integration Strategies

Hybrid Energy Systems

Multi-Energy Complementarity

Data Centre Energy System
    │
    ├── Wind Power (40-50%)
    ├── Solar PV (20-30%)
    ├── Geothermal (20-30%)
    ├── Energy Storage (10-20%)
    └── Grid Backup (<10%)

Advantages

• Stability: Multiple energy sources complement each other, improving power supply reliability
• Cost Optimization: Utilize cost characteristics of different energy sources
• Flexibility: Flexible configuration based on resource conditions

Intelligent Energy Management

Demand Response

• Load Scheduling: Adjust workloads based on renewable energy supply
• Elastic Computing: Non-critical tasks executed when renewable energy is abundant
• Predictive Scheduling: Optimize energy use based on weather forecasts

Energy Storage Systems

• Battery Storage: Smooth intermittent renewable energy supply
• Flywheel Storage: Provide rapid response capability
• Compressed Air Energy Storage: Large-scale long-term energy storage

Energy Procurement Strategies

Power Purchase Agreements (PPA)

• Price Locking: Long-term lock-in of renewable energy prices
• Risk Control: Reduce energy price volatility risks
• Carbon Credits: Obtain Renewable Energy Certificates (REC)

Virtual Power Purchase Agreements (VPPA)

• Flexibility: Not limited by geographic location
• Cost Effectiveness: Achieved through financial market trading
• Scale Effect: Joint procurement by multiple data centres

4. Circular Data Centres

Circular Data Centre Concept

Definition

Circular data centres maximize resource utilization, minimize environmental impact, and achieve resource closed-loop cycles, including:

• Energy Cycle: Renewable energy supply, waste heat recovery and utilization
• Material Cycle: Equipment recycling and reuse, closed-loop material cycles
• Water Cycle: Water recycling, zero discharge
• Carbon Cycle: Carbon neutrality, even negative carbon emissions

Energy Cycle

Waste Heat Recovery and Utilization

• District Heating: Data centre waste heat used for surrounding building heating
• Industrial Applications: Waste heat used for industrial production
• Agricultural Applications: Waste heat used for greenhouse agriculture
• Case Study: Facebook data centre in Sweden provides heating for local communities

Energy Cascade Utilization

• High-Temperature Heat Source: Used for power generation or industrial applications
• Medium-Temperature Heat Source: Used for heating or hot water
• Low-Temperature Heat Source: Used for agriculture or environmental improvement

Material Cycle

Equipment Recycling and Reuse

• Server Recycling: Old servers disassembled, components reused
• Material Recycling: Metals, plastics, and other materials recycled
• Circular Design: Consider recyclability from design stage

Closed-Loop Material Cycles

• Design Principles: Modular, standardized, easy to disassemble
• Recycling Rate Targets: Achieve 90%+ equipment recycling rate by 2026
• Material Reuse: Reduce demand for new materials

Water Cycle

Zero Water Consumption Cooling

• Air Cooling: Use natural cooling in suitable climate regions
• Closed-Loop Water Systems: Water recycling, no discharge
• Dry Cooling: Use dry coolers to reduce water consumption

Water Resource Management

• Rainwater Collection: Collect rainwater for cooling
• Reclaimed Water Reuse: Treated water recycled
• Water-Saving Technologies: Optimize cooling systems to reduce water consumption

Carbon Cycle

Carbon Neutrality Pathways

• Renewable Energy: 100% renewable energy supply
• Carbon Capture: Capture and store carbon emissions
• Carbon Offsetting: Offset carbon emissions through afforestation and other projects

Negative Carbon Emissions

• Direct Air Capture (DAC): Directly capture CO₂ from air
• Bio-Carbon Capture: Achieve negative carbon emissions through biomass energy
• Carbon Sequestration: Long-term storage of captured carbon

5. Real-World Application Cases

Global Leading Cases

Google Data Centres

• 100% Renewable Energy: Achieved 100% renewable energy supply in 2020
• Carbon Neutral: Achieved carbon neutrality in 2017
• 2030 Target: Achieve 24/7 carbon-free energy operations

Microsoft Data Centres

• Carbon Neutrality Commitment: Achieve carbon neutrality by 2030, negative carbon emissions by 2050
• Renewable Energy: Achieve 100% renewable energy supply by 2025
• Geothermal Applications: Deploy geothermal-powered data centres in Kenya and other regions

Amazon AWS

• Renewable Energy Target: Achieve 100% renewable energy supply by 2025
• Wind and Solar: Deploy large-scale wind and solar projects worldwide
• Energy Storage Systems: Combined with battery storage for stable power supply

Meta Data Centres

• Renewable Energy: Achieved 100% renewable energy supply in 2020
• Geothermal Applications: Partner with Sage Geosystems to deploy 150MW geothermal project
• Waste Heat Recovery: Deploy waste heat recovery systems in multiple data centres

Chinese Practice Cases

Alibaba Zhangbei Data Centre

• Wind + Solar: Combined wind and solar power for clean energy supply
• Liquid Cooling Technology: Adopts liquid cooling technology, PUE reduced to 1.09
• Energy Storage Systems: Deploys energy storage systems to smooth renewable energy supply

Tencent Guizhou Data Centre

• Hydropower Primary: Utilizes Guizhou's rich hydropower resources
• Solar Supplement: Deploys solar PV facilities
• Green Certification: Obtained multiple green data centre certifications

Huawei Cloud Data Centres

• Renewable Energy: Deploys renewable energy in multiple data centres
• Intelligent Scheduling: AI-driven energy scheduling systems
• Carbon Neutrality Pathway: Develops clear carbon neutrality roadmap

6. Future Development Trends (2026-2030)

Renewable Energy Technology

• Continued Cost Reduction: Expected 2026, wind and solar costs will decrease by another 20-30%
• Technology Breakthroughs: Emerging technologies such as geothermal and marine energy gradually mature
• Energy Storage Technology: Battery costs continue to decline, large-scale deployment of energy storage systems

Circular Data Centres

• 2026 Target: Major cloud service providers achieve 90%+ renewable energy supply
• 2030 Target: Achieve 100% renewable energy supply, some data centres achieve negative carbon emissions
• Material Cycles: Achieve 90%+ equipment recycling rate by 2026, achieve closed-loop material cycles by 2030

Policies and Standards

• Policy Support: Governments worldwide increase policy support for renewable energy data centres
• Standard Development: Develop circular data centre standards and certification systems
• Carbon Trading: Improve carbon trading markets to promote carbon neutrality

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Data Centres and AGI

1. AGI Requirements for Data Centres

Computing Requirements

Training Phase

• GPT-3: Approximately 3.14×10²³ FLOPS
• GPT-4: Estimated 10²⁵+ FLOPS
• Future AGI: May require 10²⁷+ FLOPS

Inference Phase

• Concurrent Users: Millions to tens of millions
• Response Time: Millisecond-level requirements
• Cost Control: Inference costs need continuous reduction

Storage Requirements

• Model Parameters: GPT-3 (175B parameters) → GPT-4 (estimated 1T+ parameters)
• Training Data: TB to PB level
• Checkpoints: Periodic saves, require massive storage

2. AGI Training Infrastructure

Hyperscale Training Clusters

Example: GPT-3 Training

• Hardware: 10,000+ NVIDIA V100 GPUs
• Training Time: Weeks to months
• Cost: Millions to tens of millions of dollars

Future AGI Training

• Hardware Requirements: May need 100,000+ GPUs
• Training Time: May take years (continuous optimization)
• Cost: May reach tens of billions of dollars

Distributed Training Challenges

• Communication Overhead: Gradient synchronization requires high-speed networks
• Fault Tolerance: Single point of failure cannot affect overall training
• Scalability: Support dynamic expansion and contraction

3. AGI Inference Infrastructure

Edge Inference

• Model Deployment: Run small models on edge devices
• Advantages: Low latency, privacy protection
• Challenges: Limited computing resources

Cloud Inference

• Model Serving: Provide services through APIs
• Load Balancing: Handle high concurrency requests
• Cost Optimization: Model compression, batch processing

4. Data Centre Evolution Direction

Dedicated AI Data Centres

• Hardware Optimization: Designed specifically for AI workloads
• Network Optimization: High-speed interconnects, support large-scale parallelism
• Software Optimization: AI framework and toolchain optimization

Green AI Data Centres

• Renewable Energy: Solar, wind power supply
• Energy Efficiency Optimization: Liquid cooling, intelligent power management
• Carbon Neutral Development: Achieve net-zero emissions

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Future Development Trends

1. Technology Trends

Hardware Evolution (2026-2030)

• More Powerful AI Chips: Expected 2026-2027, next-generation GPUs (e.g., NVIDIA X100 series, AMD MI400 series) will provide higher computing power and lower power consumption
• HBM4 Memory: Expected commercial availability 2026, further bandwidth and capacity improvements, supporting larger-scale AI models
• Optical Computing: Expected 2027-2030, optical computing technology will achieve commercialization in specific AI applications
• Quantum Computing: Expected 2026-2030, quantum computing will achieve breakthroughs in specific AI problems (e.g., optimization, search)

Software Optimization

• AutoML (Automated Machine Learning): Automated model design, widely adopted in 2024
• Federated Learning: Distributed training, protect privacy, expected to become mainstream by 2026
• Edge AI: Run AI models on edge devices, expected large-scale deployment 2026-2027
• AI-Driven Resource Scheduling: Expected 2026, AI will be fully applied to data centre resource optimization

2. Architecture Trends (2026-2030)

Cloud-Edge Collaboration

Cloud Data Centre ──┐
                     ├──> Collaborative Computing
Edge Nodes ─────────┘

• Cloud: Complex model training and inference, mature in 2024
• Edge: Real-time response, low latency, expected large-scale deployment by 2026
• Collaboration: Intelligent task allocation, expected intelligent collaboration 2026-2027

Hybrid Cloud Architecture

• Public Cloud: Elastic scaling, pay-as-you-go, widely adopted in 2024
• Private Cloud: Data security, compliance requirements, continuous development
• Hybrid: Optimal balance, expected to become mainstream enterprise choice by 2026

All-Optical Interconnect Architecture

• Optical Switching Technology: Expected 2026-2027, all-optical interconnect will be widely adopted in hyperscale data centres
• Lower Latency: Optical switching can reduce network latency to nanosecond level
• Higher Bandwidth: Support higher bandwidth density, reduce power consumption

3. Sustainable Development (2026-2030)

Green Data Centres

• Renewable Energy: Expected 2026, major cloud service providers will achieve 80%+ renewable energy supply, target 100% by 2030
• Energy Efficiency Optimization: PUE reduced to 1.05-1.1 in 2024, expected to reach 1.02-1.05 by 2026, potentially 1.01-1.02 by 2030
• Water Resource Management: Reduce water usage, recycling, expected zero water consumption cooling technology by 2026
• Carbon Capture: Expected 2027-2030, negative carbon emission technology will be piloted in data centres

Circular Economy

• Equipment Recycling: Server and component recycling, expected recycling rate 90%+ by 2026
• Material Reuse: Reduce e-waste, expected closed-loop material cycle by 2026
• Sustainable Design: Consider environmental impact from design stage, expected to become industry standard by 2026
• Prefabricated Modular Construction: Expected 2026, prefabricated and modular construction will become mainstream, reducing construction cycle by 50%+

4. Security and Compliance

Data Security

• Encryption: Transmission and storage encryption
• Access Control: Fine-grained permission management
• Audit: Complete operation logs

Compliance Requirements

• GDPR: EU data protection regulations
• CCPA: California Consumer Privacy Act
• Data Localization: Data sovereignty requirements

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Conclusion

Data centres, as computing infrastructure in the AGI era, are undergoing unprecedented transformation:

Key Technology Breakthroughs

1. Liquid Cooling Technology: Solves high power density cooling problems, PUE reduced to 1.05-1.1
2. AI-Specific Hardware: GPUs, TPUs, and other accelerators, continuous performance improvement
3. Distributed Training: Supports ultra-large-scale model training
4. Energy Efficiency Optimization: Comprehensive optimization from hardware to software

Development Trends

1. Hyperscale: Server count reaching millions
2. Specialization: Optimized for AI workloads
3. Greenification: Renewable energy, carbon neutrality goals
4. Intelligence: AI-driven automated operations

Challenges and Opportunities

Challenges:

• Massive computing and energy requirements
• High construction and operation costs
• Pressure from rapid technological iteration

Opportunities:

• Drive hardware and software innovation
• Create new business models
• Promote sustainable development technologies

Outlook

As AGI technology continues to develop, data centres will continue to evolve, becoming key infrastructure supporting the artificial intelligence revolution. Future data centres will be more intelligent, efficient, and green, providing powerful momentum for human society's digital transformation.

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References

Technical White Papers and Research Reports

1. HBM Technology

   • PDSC2 Introduction to HBM (2024)
   • HBM3/HBM4 Technical Specifications

2. GPU Architecture

   • AMD CDNA3 Architecture White Paper (2024)
   • NVIDIA H100/H200 Technical Documentation

3. Data Centre Architecture

   • AWS Well-Architected Framework - High Performance Computing Lens (2024)
   • Juniper Networking the AI Data Center (2024)
   • HPC Centre Redundancy & Reliability White Paper

4. Cooling Technology

   • Sandia Labs - Holistic Data Center Design (2024)
   • Cooling Equipment Technology Research Reports

5. Storage Technology

   • Tech Brief: 3D NAND Technology
   • High Performance Computing Solution Resources

6. Market Data

   • Statista Data Centers Dossier (2024-2025)
   • Global Ethernet Switch Market Share (Q1 2025)
   • Network Security Equipment Spending (2016-2024)

Industry Reports

1. Uptime Institute - Data Center Industry Reports (2024-2025)
2. Google - Data Center Efficiency Best Practices
3. Microsoft - Azure Data Center Architecture
4. NVIDIA - AI Infrastructure Solutions
5. OpenAI - GPT-4 Technical Report
6. China Academy of Information and Communications Technology - Data Center White Paper (2024)

Latest Technology Trends

• High Performance Computing Market Analysis (2024-2031)
• AI Data Centre Network Architecture Evolution
• Liquid Cooling Technology Application Cases (2024-2025)
• HBM Memory Technology Development Roadmap

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Article Note

This article is written based on existing research materials (including the latest white papers, technical reports, industry data in the HPC high-speed computing field, etc.) combined with AI generation technology.

The comparison tables, technical indicators, market data, etc. in this article are compiled based on publicly available research materials and industry reports, aiming to provide readers with clear and systematic technical comparisons and trend analysis.

Last Updated: November 2025