2025 AI Network Technology White Paper

Part I: Network for AI - New Network Infrastructure for AI

This section focuses on how AI workloads are driving the evolution of network infrastructure, with specific requirements for ultra-high bandwidth, low latency, and deterministic transmission.

Chapter 1: AI and Network Integration Evolution

The integration of AI and networks has evolved through three stages: initial exploration (2012-2016), rapid development (2017-2021), and deep integration (2022-present). Each stage has distinct technical characteristics and industry milestones.

Key developments include the breakthrough of deep learning in 2012, the emergence of Transformer architecture in 2017, and the global impact of ChatGPT in 2022, which accelerated the construction of low-latency inference networks and specialized AI computing centers.

Chapter 2: Network Architecture Transformation Driven by AI

AI workloads present new requirements for network infrastructure across training, inference, and edge scenarios. Training scenarios require massive "east-west" traffic for parameter synchronization, while inference scenarios prioritize low-latency "north-south" traffic for user interactions.

The white paper examines key network topologies for AI clusters, including Fat-Tree and Dragonfly architectures, each with distinct advantages for different scale requirements.

Chapter 3: High-Performance Network Technologies for AI

This chapter systematically explains the high-performance network technologies supporting AI innovation and large-scale applications, covering:

• Scale Up Technologies: NVLink, UALink, SUE, OISA, ALS, and ETH+ for intra-node and near-node communication
• Scale Out Technologies: InfiniBand, RoCEv2, and UEC transmission protocols for inter-node communication
• Breakthrough Technologies: Deterministic WAN, super node computing architecture, and 6G-AI network coordination

Chapter 4: Network for AI Application Practices

The white paper presents real-world implementations from leading companies, including:

• Mobile Cloud's new intelligent computing network architecture HPN1.0
• China Telecom's Tianyi Cloud intelligent computing project
• Alibaba Cloud's HPN7.0 intelligent computing network
• Kiwi Fabric's full-stack AI Networking solution
• DYXnet's private AI implementation for education enterprises
• WeBank's financial-grade intelligent computing AI network
• YsChip's innovative smart NIC solutions

Chapter 5: Future Development and Outlook

Future trends for Network for AI include deeper integration of networking and computing, customized AI network evolution, open ecosystem collaboration, and green low-carbon network infrastructure.

Strategic recommendations focus on promoting technical standardization and modularization, breaking through key technical bottlenecks, and emphasizing cross-domain technical collaborative innovation.

Part II: AI for Network - AI-Enabled Network Intelligence Upgrade

This section explores how AI technologies are enhancing network intelligence, automation, and autonomous operations.

Chapter 6: AI-Driven Network Intelligence Development

Network intelligence is evolving to address challenges of scale, complexity, operational efficiency, and security threats. The autonomous network level framework defines six stages from L0 (manual operations) to L5 (fully autonomous network).

The network intelligence upgrade process follows four stages: comprehensive perception, intelligent analysis, autonomous decision-making, and execution with assurance.

Chapter 7: Key Technologies for AI-Enabled Networks

Key technologies driving network intelligence include:

• Intent-Driven Networks (IDN): Translating business objectives into network configurations
• Digital Twin Networks (DTN): Creating virtual replicas of physical networks for simulation and optimization
• Intelligent Network Large Models: Applying large language models to network operations, performance optimization, and security
• Federated Learning: Enabling collaborative model training without sharing raw data

Chapter 8: AI for Network Application Practices

Real-world implementations of AI for Network technologies include:

• China Unicom's AI agent for subway wireless network optimization
• China Mobile's Jiutian large model for wireless network optimization
• China Tower's intelligent network operation and optimization platform
• Huawei's Galaxy AI network solution
• ZTE's AIR Net autonomous network evolution solution
• JD Cloud's JoyOps intelligent operations

Chapter 9: Challenges and Future Trends

Future trends for AI for Network include deep application of large models in the network field, autonomous evolutionary network intelligence systems, popularization of Network as a Service (NaaS) models, and ecosystem openness and collaborative innovation.

Strategic recommendations focus on deepening basic theoretical research, promoting core technology breakthroughs, and building open innovation platforms.

Part III: Future Outlook

The concluding section presents a forward-looking perspective on AI network development.

Chapter 10: Top 10 Trends in AI Network Development

The white paper identifies ten key trends shaping the future of AI networks:

1. From general interconnection to intelligent computing center network paradigm
2. From independent layers to hyper-converged centerless architecture
3. From best-effort to deterministic intelligent protocol stack
4. From cloud-centric to ubiquitous cloud-edge-end intelligent collaboration
5. From passive configuration to intent-driven intelligent scheduling
6. From data concentration to privacy-first federated collaboration paradigm
7. From functional add-ons to native intelligent network kernel
8. From one-size-fits-all to knowledge-internalized industry-specific networks
9. From traditional Internet to agent Internet
10. From separated computing and networking to integrated computing-networking-power symbiosis