Preparing data center networking infrastructure for AI workloads presents multiple challenges. Up to 33% of elapsed time in AI/ML tasks is often wasted waiting for network availability, resulting in costly GPU resources remaining idle¹. Furthermore, AI application traffic is experiencing exponential growth, doubling every two years, while cluster sizes are expanding fourfold, imposing tremendous demands on network infrastructure².
Organizations struggle with the risk of either under- or over-provisioning AI infrastructure due to a lack of predictive tools and methodologies for future AI workload demands. Additionally, they may not have sufficient in-house expertise in cutting-edge network technologies like NVLink, InfiniBand, 400/800 Gb Ethernet and SONiC.
We’ve developed a holistic approach to designing AI networks around your use cases: Dell Design Services for AI Networking. This addition to our Dell AI Factory services helps you design your AI networking to ensure optimal network performance. Let’s explore some of the key elements we focus on when designing networks for your AI workloads.
Needs: Bandwidth Boosts, Minimized Latency & Lossless Transmission
Enterprise use cases include a mix of AI inferencing and training activities. During inferencing, a trained AI model applies its learned parameters, weights, or rules to transform the input data into meaningful information or actions. A network carrying inferencing traffic requires low latency for real-time responsiveness and high bandwidth when using larger models.
Complex AI training workloads require extreme bandwidth and parallel processing to synchronize calculations among the many GPUs in a cluster. The ‘elephant flows’ generated by GPU synchronization are driving transformation in data center networking, creating needs for unprecedented bandwidth boost, minimized latency, and lossless data transmission.
Attributes of AI Network Fabrics
AI back-end fabrics need to be engineered to address the challenges posed by AI model training. These fabrics require high capacity and low latency. Network designers need to consider tail latency, which occurs when a few unusual requests slow down processing.
To achieve these requirements, AI fabrics utilize non-blocking architectures and 800 Gb/s switching backplanes with optional 400Gb/s breakouts. Advanced features such as Remote Direct Memory Access (RDMA) Over Converged Ethernet (RoCEv2) are employed. RDMA is also a key component of InfiniBand, a high-speed, low-latency networking technology. InfiniBand and 400/800 Gb Ethernet are two major AI training fabric alternatives.
Handling network congestion is vital in AI networks. Explicit Congestion Notification (ECN) gives early warning of a network congestion condition, while Priority-based Flow Control (PFC) enables network software to pause transmissions until the network can ‘catch up.’ Other advanced techniques that may come into play include adaptive routing, dynamic load balancing, enhanced hashing modes, and packet/cell spraying.
Effective management and orchestration of these networks start with zero-touch provisioning and automatic deployment, enabling seamless scalability. Advanced network monitoring tools provide early visibility into potential issues or anomalies, ensuring the network remains robust and reliable under heavy AI workloads.
Strategic Planning for Future-Ready AI Networks
As is always the case with major technological shifts, success requires diligent, thorough analysis and planning.
The first step is a thorough audit of your current network infrastructure. This process involves evaluating capabilities, limitations, AI use cases, workload types, growth trajectories, and geographical footprint. Identifying integration points for new AI network components is crucial during this assessment.
The next step involves crafting a vision of your desired future network. This requires an in-depth analysis of AI usage patterns, workload types, and performance considerations. A comprehensive GPU network design, along with integration guidance, is essential for seamless network scaling as demand escalates.
Finally, develop a robust AI network strategy that includes network design, connectivity options, and technology choices. This strategy should address scaling needs and growth management, ensuring a resilient and adaptable network framework capable of meeting future demands.
Access Extensive AI Network Experience and Expertise with Dell Services
Partnering with expert consultants can provide the specialized knowledge and technical expertise required to help you optimize AI network performance, integrate innovative technologies, and maintain robust security measures to deliver the infrastructure performance and reliability expected by your AI use cases. Optimizing AI network infrastructure is critical to building an AI Factory that systematically delivers AI-empowered use cases and produces more efficient workflows and improved business outcomes. Dell Technologies AI experts can help accelerate your progress towards AI outcomes at every stage, from strategy to technology architectures, data management, use case deployments, and adoption and change management. To ensure the completeness of your AI solutions, we leverage Dell’s robust ecosystem of partners.
Check out the ways Dell Services can collaborate with your team to smooth your networking journey into an AI-driven future.
1 Meta report on AI data and networking, 2023, Link from Dell’Oro Group
2 Dell’Oro Group Networking report, May 2024, Link from RCR Wireless News
