When understood, these six concepts will help anyone new to ACI to understand a more detailed technical discussion.

Widely available GPU-accelerated servers, combined with better hardware and popular programming languages like Python and C/C++, along with frameworks such as PyTorch, TensorFlow and JAX, simplify the development of GPU-accelerated ML applications. These applications serve diverse purposes, from medical research to self-driving vehicles, relying on large datasets and GPU clusters for training deep neural networks. Inference frameworks apply knowledge from trained models to new data with optimized clusters for performance. The learning cycles involved in AI workloads can take days or weeks, and high-latency communication between server clusters can significantly impact completion times or result in failure. AI workloads demand low-latency, lossless networks, requiring appropriate hardware, software features, and configurations. This article will explain advanced queueing solutions used by all the major OEMs in the Network Operating Systems (NOS) that support ECN and PFC.

RoCEv2 is a solution for achieving swift data throughput and minimal delay in modern data centers. It incorporates features like Priority Flow Control (PFC) and Explicit Congestion Notification (ECN) to establish a lossless network environment. PFC manages data flow at the interface level, while ECN detects and mitigates congestion before PFC activation becomes necessary. The combination of ECN and PFC, known as Data Center Quantized Congestion Notification (DCQCN), optimizes congestion management in RDMA networks. Careful tuning of queue thresholds is crucial to prevent hot spots and ensure low Job Completion Times (JCTs). The use of ECN and PFC is necessary for maintaining a lossless fabric in GPU-to-GPU communication during AI/ML training runs.

The article below covers VXLAN encapsulation and how MP-BGP is used to learn and forward layer two and layer three traffic across the encapsulation. To better understand the content, we recommended that readers have prior knowledge of BGP and the MP-BGP routing protocol. For those unfamiliar with these concepts, we suggest reading our foundational learning path's "MP-BGP for the beginner" article beforehand. It's important to note that MP-BGP EVPN VXLAN may initially seem intimidating, but this beginner's guide will clearly understand how it works. It's a technology that has gained popularity over the last few years, with many companies adopting it.

This article discusses the importance of deploying AI applications and training models using distributed computing and the need for significant computational resources. It highlights the role of network efficiency and scalability in large-scale AI deployments.

AI workloads require significant data and computational power, with billions of parameters and complex matrix operations. Inter-network communication accounts for a significant portion of job completion time. Traditional network architectures are insufficient for large-scale AI training, necessitating investments in new network designs. Arista Networks offers high-bandwidth, low-latency and scalable connectivity for GPU servers, with features like Data Center Quantized Congestion Notification and intelligent load balancing. Arista's AI Leafs and Spines provide high-density and high-performance switches for AI networking. Different network designs are recommended based on the size of the AI application. A dedicated storage network is recommended to handle the large datasets used in AI training. Arista's Cloud Vision Portal and AI Analyzer tools provide automated provisioning and deep flow analysis. Arista's IP/Ethernet switches are well-suited for AI/ML workloads, offering energy-efficient interconnects and simplified network management.

Cisco Unified Edge with Intel Xeon 6 delivers a future-ready AI infrastructure, boosting performance and security while reducing network traffic through real-time inferencing at the data source.

This series of articles will explore what intent-based networking (IBN) is and how organizations can leverage it to build multi-domain architectures. As more organizations embrace these architectures, WWT is seeing a change in networking the likes which haven't been seen in many years.

In part one of this blog series that outlines the key role that networking plays in AI/ML workloads, we examine data transfer protocols and congestion considerations.

Juniper's AI-Native Networking Platform leverages structured data for better AI models, providing a strong foundation for future AIOps applications.

Opengear's survey of over 1,000 executives and engineers reveals growing investments and highlights the need for collaboration, clear leadership, and strategic alignment to fully harness AI's potential in boosting efficiency, security, and network resilience.

Introducing Juniper's AI-Native Networking Platform, which extends AI throughout our whole portfolio to assure experiences and simplify operations end-to-end across every network domain.