Segment Routing accomplishes the same thing as MPLS, but is less complex, extremely robust and can be scaled without any limitations.

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

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.

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.

Many organizations are considering some form of network as a service (NaaS) to address cost, staffing, and technology needs. A predictable and flexible monthly bill with third party management is attractive, especially for C-level executives and application and business managers who want to move to a NaaS model.

An in-depth guide for IT decision makers navigating the complex IT landscape.

Learn about the components of high-performance networking, including available transport technologies and the secret to making things go fast.

WWT + F5 can help you protect your organization while providing greater flexibility, agility, and scalability

5 ways to gain new efficiencies and insights

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.

Equinix Fabric connects distributed infrastructure to global service providers and partners. Discover how the network infrastructure can help your business.

WDM technologies allow organizations to place equipment at either end of a fiber pair and combine multiple wavelength channels on a single fiber pair instead of using multiple separate fibers pairs for every separate service.