Time to read: 8 minutes 43 seconds | Published: October 16, 2025
AI Data Center Networking What is AI data center networking?
AI data center networking refers to the data center networking fabric that enables artificial intelligence (AI). It supports the rigorous network scalability, performance, and low latency requirements of AI and machine learning (ML) workloads, which are particularly demanding in the AI training phase.
In early high-performance computing (HPC) and AI training networks, InfiniBand, a high-speed, low-latency, proprietary networking technology, initially gained popularity for its fast and efficient communication between servers and storage systems. Today, the open alternative is Ethernet, which is gaining significant traction in the AI data center networking market and is expected to become the dominant technology.
There are multiple reasons for Ethernet’s growing adoption, but performance, operations and cost stand apart. The talent pool of network professionals who can build and operate an Ethernet versus a proprietary InfiniBand network is massive, and a broad array of tools are available to manage such networks compared to InfiniBand technology, which is sourced primarily via Nvidia.
What AI-driven requirements are addressed by AI data center networking?
Generative AI (GenAI) is proving to be a transformative technology around the world. Generative AI, and large deep-learning AI models in general, bring new AI data center networking requirements. There are three phases to developing an AI model:
- Phase 1: Data preparation–Gathering and curating data sets to be fed into the AI model.
- Phase 2: AI training–Teaching an AI model to perform a specific task by exposing it to large amounts of data. During this phase, the AI model learns patterns and relationships within the training data to develop virtual synapses to mimic intelligence.
- Phase 3: AI inference–Operating in a real-world environment to make predictions or decisions based on new, unseen data.
Phase 3 is generally supported by existing data center and cloud networks. However, Phase 2 (AI training) requires extensive data and compute resources to support its iterative process, where the AI model learns from continuously gathered data to refine its parameters. Graphics processing units (GPUs) are well suited for AI learning and inference workloads but must work in clusters to be efficient. Scaling up clusters improves the efficiency of the AI model but also increases cost, so it is critical to use high-performance, low-latency AI data center networking that does not impede the cluster’s efficiency.
Many, even tens of thousands of GPU servers (with costs exceeding $400,000 per server in 2023), must be connected to train large models. As a result, optimizing job completion time (JCT) and minimizing or eliminating tail latency (a condition where outlier AI workloads slow the completion of the entire AI job) are keys to optimizing the return on GPU investment. In this use case, the AI data center network must be 100% reliable and cause no degradation in cluster efficiency.
How does AI data center networking work?
Although expensive GPU servers typically drive the overall cost of AI data centers, AI data center networking is critical because a high-performing network is required to maximize GPU utilization. Ethernet is an open, proven technology best suited to provide this solution within a data center network architecture optimized for AI. The enhancements include congestion management, load balancing, and minimized latency to improve JCT. Finally, simplified management and automation ensure reliability and continued performance.
- Fabric design: AI data centers can adopt various fabric architectures, but an any-to-any non-blocking Clos fabric is recommended to optimize performance for large-scale training. Most AI clusters today use a fully rail-optimized design, ensuring predictable performance and consistent bandwidth. These fabrics are built with uniform networking speeds of 400 Gbps (moving to 800 Gbps and 1.6 Tbps) from the NIC to the leaf and through the spine. Depending on the model size and GPU cluster scale, a two-layer, three-stage non-blocking fabric or three-layer, five-stage non-blocking fabric can be deployed to deliver high-throughput and low-latency.
- Flow control and congestion avoidance: In addition to fabric capacity, additional design considerations increase the reliability and efficiency of the overall fabric. These considerations include properly sized fabric interconnects with the optimal number of links and the ability to detect and correct flow imbalances to avoid congestion and packet loss. Explicit congestion notification (ECN) with data center quantized congestion notification (DCQCN) plus priority-based flow control resolve flow imbalances to ensure lossless transmission.
To reduce congestion, dynamic and adaptive load balancing is deployed at the switch. Dynamic load balancing (DLB) redistributes flows locally at the switch to distribute them evenly. Adaptive load balancing monitors flow forwarding and next hop tables to identify imbalances and steer traffic away from congested paths.
When congestion is not avoided, ECN provides early notification to applications. During these periods, leafs and spines update ECN-capable packets to notify senders of the congestion, which causes the senders to slow transmission to avoid packet drops in transit. If the endpoints do not react in time, priority-based flow control (PFC) allows Ethernet receivers to share feedback with senders on buffer availability. Finally, during periods of congestion, leafs and spines can pause or throttle traffic on specific links to reduce congestion and avoid packet drops, enabling lossless transmissions for specific traffic classes.
- Scale and performance: Ethernet has emerged as the open-standard solution of choice to handle the rigors of high-performance computing and AI applications. It has evolved over time (including the current progression to 800 GbE and 1.6 TE) to become faster, more reliable, and scalable, making it the preferred choice for handling high data throughput and low-latency requirements necessary for mission-critical AI applications.
- Automation: Automation is the final piece for an effective AI data center networking solution, though not all automation is created equal. For full value, the automation software must provide experience-first operations. It is used in design, deployment, and management of the AI data center on an ongoing basis. It automates and validates the AI data center network lifecycle from Day 0 through Day 2+. This results in repeatable and continuously validated AI data center designs and deployments that not only remove human error but also take advantage of telemetry and flow data to optimize performance, facilitate proactive troubleshooting, and avert outages.
HPE Juniper Networking AI data center networking solution builds upon decades of networking experience and AIOps innovations
Juniper’s AI data center networking solution builds upon our decades of networking experience and AIOps innovations to round out open, fast, and simple-to-manage Ethernet-based AI networking solutions. These high-capacity, scalable, non-blocking fabrics deliver the highest AI performance, fastest job completion time, and most efficient GPU utilization. The Juniper AI data center networking solution leverages three fundamental architectural pillars:
- Massively scalable performance–To optimize job completion time and therefore GPU efficiency.
- Industry-standard openness–To extend existing data center technologies with industry-driven ecosystems that promote innovation and drive down costs over the long term.
- Experience-first operations–To automate and simplify AI data center design, deployment, and operations for back-end, front-end, and storage fabrics.
These pillars are supported by:
- A high-capacity, lossless AI data center network design taking advantage of an any-to-any non-blocking Clos fabric, the most versatile topology to optimize AI training frameworks.
- High-performance switches and routers, including HPE Juniper PTX Series Routers, based on Juniper Express Silicon for the spine/super spine, and QFX Series Switches, based on Broadcom’s Tomahawk ASICs as leaf switches providing AI server connectivity.
- Fabric efficiency with flow control and collision avoidance.
- Open, standards-based Ethernet scale and performance with 800 GbE.
- Extensive automation using Apstra® Data Center Director intent-based networking software to automate and validate the AI data center network lifecycle from Day 0 through Day 2+.
AI data center networking FAQs
What problem does AI data center networking solve?
AI data center networking solves the performance requirements of generative AI and large deep-learning AI models in general. AI training requires extensive data and compute resources to support its iterative process in which the AI model learns from continuously gathered data to refine its parameters. Graphics processing units (GPUs) are well suited for AI learning and inference workloads but must work in clusters to be efficient. Scaling up clusters improves the efficiency of the AI model but also increases cost, so it is critical to use AI data center networking that does not impede the efficiency of the cluster.
Many, even tens of thousands of GPU servers (with costs exceeding $400,000 per server in 2023) must be connected to train large models. As a result, maximizing job completion time and minimizing or eliminating tail latency (a condition where outlier AI workloads slow the completion of the entire AI job) are keys to optimizing the return on GPU investment. In this use case, the AI data center network must be 100% reliable and cause no efficiency degradations in the cluster.
What are the benefits AI in data center networking?
AI in data center networking has many benefits including:
- Improved efficiency: AI algorithms dynamically alter network settings to optimize traffic, minimize latency, and boost efficiency.
- Scalability: By managing resources depending on demand and workload, AI-driven automation improves data center scalability.
- Cost savings: AI can reduce network maintenance and administration expenses by automating regular jobs and optimizing resource use.
- Enhanced security: AI can detect and respond to threats in real-time, reducing network breaches and attack risks.
- Predictive capabilities: AI's predictive analytics allow data centers to build and maintain networks based on anticipated demands and concerns.
AI data center networking transforms network infrastructure management and optimization using machine learning and AI to improve efficiency, scalability, security, and cost.
What are the advantages of Ethernet over InfiniBand for AI data center networking?
In early high-performance computing (HPC) and AI training networks, InfiniBand, a high-speed, low-latency, proprietary networking technology initially gained popularity for its fast and efficient communication between servers and storage systems. Today, the open alternative Ethernet is gaining significant traction in the modern AI data center networking market and is expected to become the dominant technology.
While proprietary technologies like InfiniBand can bring advancements and innovation, they are expensive, charging premiums where competitive supply-and-demand markets can’t regulate costs. In addition, the talent pool of network professionals who can build and operate an Ethernet versus a proprietary InfiniBand network is massive, and a broad array of tools are available to manage such networks compared to InfiniBand technology, which is sourced primarily via Nvidia.
Next to IP, Ethernet is the world's most widely adopted networking technology. Ethernet has evolved to become faster, more reliable, and scalable, making it preferred for handling the high data throughput and low-latency requirements of AI applications. The progression to 800GbE and 1.6 T Ethernet enhancements enable high-capacity, low-latency, and lossless data transmission, making Ethernet fabrics highly desirable for high-priority and mission-critical AI traffic.
What is the future of AI data center networking?
- AI-driven network automation: AI will improve network automation, eliminating manual involvement and improving operational efficiency.
- Edge AI: As edge computing expands, AI will analyze data locally at the network edge, lowering latency and boosting real-time decision-making.
- AI for cybersecurity: Advanced threat detection, real-time anomaly identification, and automated incident response will improve network security.
- 5G and beyond: AI-driven network management will help 5G and future networks handle complexity and data volumes.
- Self-optimizing networks: AI will enable networks to alter settings, forecast faults, and optimize performance without human involvement.
- Sustainability: AI will optimize energy and cooling systems in data centers, decreasing environmental effects.
- AI-enhanced network analytics: Advanced AI analytics will improve decision-making by revealing network performance, user behavior, and upcoming patterns.
AI implementation in data center networking is complex, but strategic approaches and best practices might assist. AI data center networking is poised for automation, security, and efficiency.
What products and solutions does HPE Juniper Networking provide for AI data center networking?
The HPE Juniper Networking AI data center networking solution provides a high-capacity, lossless AI data center network design that uses an any-to-any non-blocking Clos fabric, the most versatile topology to optimize AI training frameworks. The solution takes advantage of high-performance, open standards-based Ethernet switches and routers with interfaces up to 800 GbE. In addition, it uses Apstra Data Center Director intent-based networking software to automate and validate the AI data center network lifecycle from Day 0 through Day 2+.
What are the AI data center networking key considerations?
Key considerations for organizations planning to adopt AI in their data center networks:
- Assess business needs and objectives: Understand the specific goals and objectives for adopting AI in data center networking. Define success as improving efficiency, security, cost savings, or scalability.
- Evaluate current infrastructure and readiness: Assess hardware, software, and data architecture for AI integration readiness. Identify any gaps or areas that can require improvements or modifications.
- Data quality and availability: Provide high-quality data for AI model training and decision-making. Data governance policies provide data integrity, security, and compliance.
- Security and privacy considerations: When deploying AI solutions, prioritize cybersecurity and data privacy. Develop secure AI systems that meet norms and requirements.
- AI integration and compatibility: Create a thorough integration plan to integrate AI into network systems smoothly. Think about legacy infrastructure compatibility and future technology interoperability.
- Skills and training: Assess the company's AI skills and identify gaps. Help it professionals learn how to manage and use AI-driven technologies.
- Start with pilot projects: Test AI applications using modest pilot projects in real-world circumstances. Pilot programs test AI systems, uncover issues, and improve implementation tactics before deployment.
- ROI and cost: Evaluate ROI and TCO for AI deployment. Consider infrastructure, software licenses, maintenance, and training costs.
- Vendor selection and partnerships: Select reputable suppliers and technology partners with demonstrated AI and data center networking competence. Collaborate closely to align with company goals and harness vendor support for effective implementation.
- Monitoring and continuous improvement: Track AI solutions' commercial results using metrics and KPIs. Continuously improve through data-driven assessments, updates, and optimizations.
By addressing these characteristics, enterprises can plan and implement AI in their data center networks to maximize performance, efficiency, and security while minimizing risks.