Architecting Enterprise-Ready, High-Performance Networks with Arista and Intelligent Visibility

Table of Contents

Frequently Asked Questions - FAQs

The Network is the New AI Accelerator

AI is pushing networks to their limits. The right architecture isn’t just an IT choice — it’s a business decision that defines how quickly enterprises turn AI potential into real outcomes.

The rise of large-scale AI and machine learning is transforming how data centers are built. Networking is no longer just about connecting devices — it’s now a critical part of the compute fabric itself. Massive, parallel data flows from AI training and inference are overwhelming traditional network designs, causing costly slowdowns that leave expensive GPUs idle. The performance of the network interconnect has become the key factor that determines whether AI investments deliver real ROI.

This report dives into the specific demands of AI networking and makes the case for open, standards-based, lossless Ethernet as the best architecture for enterprises aiming to scale AI. This approach offers the right mix of speed, scalability, operational simplicity, and cost-efficiency — from early pilots to full-scale deployments.

Arista Networks is leading this shift with a complete portfolio of hardware and software engineered for AI. With high-performance switches, a unified operating system (Arista EOS®), and robust network-wide management and visibility through Arista CloudVision®, Arista delivers a blueprint for building an AI Center that performs under pressure.

But technology alone isn’t enough. Designing and operating an AI network takes deep technical expertise. That’s where Intelligent Visibility comes in. Our team provides design, deployment, automation, and co-managed services that help organizations turn Arista’s capabilities into real business value, avoid costly mistakes, and keep AI infrastructure performing at its peak.

Why Traditional Networking Doesn't Work for AI

The explosion of growth in GPU clusters has brought in a new kind of workload that behaves nothing like the traditional client-server traffic patterns that have shaped enterprise networks for decades. To build a network that truly powers AI innovation, it’s critical to understand how AI traffic works and why old network designs simply can’t keep up.

The Nature of AI Traffic

Training modern AI models, especially deep learning and large language models, depends on massive parallelism. Instead of one powerful server running the workload, thousands of GPUs work together, each tackling a piece of the problem. These GPUs constantly exchange huge amounts of data, including model parameters, gradients, and segments of training data, to stay in sync.

This creates an enormous surge of east-west traffic, with data flowing between servers inside the data center. That’s a big shift from the north-south flows of older applications, where most traffic traveled between external clients and internal servers.

AI workloads generate very specific communication patterns:

All-to-All: Every GPU in a training group needs to talk to every other GPU, sending and receiving updates.
All-Reduce: Each GPU calculates results locally, then those results are combined and shared back across the entire cluster.

These patterns often cause a problem called incast congestion, where multiple nodes send data to a single destination at the same time, overwhelming switch buffers and causing either delays or dropped packets.

The High Cost of Network Bottlenecks

In AI infrastructure, the key metric is Job Completion Time, or JCT. The faster a model completes training or a large inference job finishes, the sooner the organization can put insights to work. But there’s a catch. AI training is a synchronized process, and the entire cluster moves only as fast as its slowest GPU. If one GPU has to wait for data, the rest of the cluster sits idle.

This isn’t just a technical challenge. It’s a massive financial issue. High-end GPUs can cost tens of thousands of dollars each, and large clusters often include thousands of them. Studies show AI jobs can spend 30 to 50 percent of their time just waiting on the network to deliver data. That’s millions of dollars in idle hardware doing nothing useful.

A single burst of congestion can introduce tail latency — the slowest packets in a flow — which delays one GPU and stalls the entire training job. In AI networks, it’s not the average delay that kills performance. It’s these rare but crippling slowdowns.

All of this changes the economics of networking. In traditional IT, networks were considered a cost center, with the goal of getting the lowest cost per port. In an AI data center, the network becomes a critical factor in maximizing return on massive GPU investments. Spending more on purpose-built networking can pay back significantly by reducing idle time, shortening job completion times, and speeding up results.

In AI, the network isn’t just connecting boxes. It’s the lever that decides how quickly enterprises can turn data into breakthroughs.

Key Network Requirements for AI

To avoid bottlenecks and keep AI infrastructure running at full speed, modern networks need to deliver on four key fronts:

High Throughput and Bandwidth: Modern GPUs demand speeds of 400 gigabits per second, 800 gigabits, or higher. A single GPU node may require more than one terabit per second of combined bandwidth to operate at full potential. Without enough bandwidth, GPUs stall waiting for data.
Low and Predictable Latency: AI clusters rely on tight synchronization. While InfiniBand has long offered sub-2-microsecond latencies, new Ethernet fabrics are getting close. Predictable low latency is crucial to keep large clusters moving in step.
Lossless Transmission: Packet loss is a deal-breaker for AI workloads. Unlike many traditional applications that can handle dropped packets, AI training depends on every bit arriving correctly. A single lost packet might require resending huge data blocks, increasing JCT or crashing a job. AI networks must be engineered for lossless traffic.
Massive Scalability: AI clusters are growing quickly, from small test environments to data centers with tens of thousands of GPUs. Networks must scale smoothly without rearchitecting or sacrificing performance.

Why Traditional Networks Fall Short

Legacy enterprise networks weren’t designed for AI’s demanding traffic. They’re built around hierarchical, multi-tiered architectures that work fine for typical north-south traffic but break down when facing heavy east-west flows.

These traditional designs fall short in several ways:

Congestion Handling: Older networks rely on buffering and dropping packets when overwhelmed. That’s unacceptable for AI, where dropped packets can stall entire workloads.
Shallow Buffers: Many legacy switches lack the deep buffers needed to handle the microbursts typical of AI data exchanges, leading to packet loss.
Inefficient Load Balancing: Conventional load balancing techniques like ECMP hashing often fail to distribute AI’s low-entropy traffic evenly, leaving some paths overloaded while others are idle.
Limited Visibility: Traditional networks don’t offer the detailed, real-time telemetry required to diagnose and fix AI-specific performance problems.

AI workloads have fundamentally different demands. They need networks that are flat, non-blocking, lossless, and intelligent. Trying to run large-scale AI on a traditional network is a recipe for poor performance, wasted investment, and missed opportunities.

The Interconnect Crossroads: Open Ethernet vs. Proprietary Fabrics

As companies design their AI infrastructure, one of the biggest choices they face is how to connect everything physically. The question comes down to this: should they build on open, widely used Ethernet, or invest in a proprietary, high-performance fabric like InfiniBand?

InfiniBand has long been associated with high-performance computing thanks to its reputation for low latency and lossless data handling. But today’s analysis shows that enhanced, standards-based Ethernet has become the smarter choice for enterprise AI centers. It offers a better balance of performance, scalability, operational simplicity, and cost-efficiency.

At hyperscale, Ethernet isn’t settling for second best. It’s delivering the performance enterprises need, with the flexibility and openness they can’t afford to live without.

Breaking the Performance Myth

For years, InfiniBand’s edge was simple: lower latency and lossless transmission. But that advantage has shrunk significantly. Modern  Ethernet, especially when enhanced with technologies like RoCEv2 (Remote Direct Memory Access over Converged Ethernet), can now deliver sub-microsecond latency that rivals InfiniBand. And when it comes to sheer bandwidth, Ethernet is ahead of the curve. Platforms offering 800 gigabits per second and even 1.6 terabits are already available from multiple vendors, sometimes ahead of comparable InfiniBand solutions.

Real-world deployments back this up. In side-by-side testing the performance difference between Ethernet and InfiniBand in MLPerf benchmarks to be statistically insignificant. Even more compelling, Meta has publicly shared that it built a 24,576-GPU cluster for training Llama 3 on an Arista Ethernet fabric and matched the performance of its InfiniBand-based systems, without hitting any network bottlenecks. These results show that Ethernet isn’t a compromise. It’s a proven, high-performance solution for AI at massive scale.

Why Openness Wins for the Enterprise

Beyond pure speed, the real strength of Ethernet is its openness. While InfiniBand remains a proprietary technology largely controlled by Nvidia, Ethernet is governed by open standards under the IEEE. This has created a thriving, multi-vendor ecosystem where companies like Arista, Broadcom, Intel, and many others compete at every layer of the stack. The result is faster innovation, more choice, and lower costs for enterprises.

Vendor lock-in is a serious concern with InfiniBand. Customers often end up tied to a single vendor for switches, NICs, cables, and management software (as well as their GPUs). That lack of competition limits flexibility and gives one vendor significant pricing power. In contrast, Ethernet’s open standards prevent lock-in and allow enterprises to swap in the best products for each layer of the network without being held hostage to a single vendor’s roadmap or pricing.

The Practical Benefits of Ethernet

Nearly every enterprise already relies on Ethernet across its data center. Their teams are trained on it, and their tools for monitoring, automation, and security are built around it. Dropping a specialized InfiniBand “island” into the middle of the environment creates significant challenges. It demands specialized skills, separate management tools, and complex gateways to connect back into the broader network.

An all-Ethernet AI center avoids those headaches. It provides a unified operational model, allowing organizations to leverage their existing expertise and tools. This simplicity dramatically lowers operational expenses and total cost of ownership. While InfiniBand hardware can cost twice as much upfront, the long-term operational overhead often creates an even bigger financial gap.

Ethernet Delivers Scalability and Flexibility

Ethernet also wins on scalability and future-proofing. Modern Ethernet-based leaf-spine architectures, including Arista’s Distributed Etherlink Switch (DES), can scale to hundreds of thousands of GPUs within a single fabric. That exceeds the practical limits of traditional InfiniBand designs, which often top out around 40,000 nodes before requiring proprietary gateways and added complexity.

Ethernet networks also offer investment protection. If priorities shift, the same switches can be repurposed for general compute, storage, or other workloads. That’s far harder to achieve with proprietary InfiniBand equipment.

This shift mirrors what happened decades ago when enterprises moved away from proprietary mainframes to open x86 servers. Mainframes once led in performance, but the open x86 ecosystem ultimately won out, thanks to lower costs, greater flexibility, and innovation driven by healthy competition. AI networking is heading down the same path, with open Ethernet poised to become the standard for high-performance infrastructure.

Table: Ethernet vs InfiniBand for AI Networking

Redefining Performance

Today, the definition of “performance” goes far beyond just raw latency measured in nanoseconds. True business performance is about finishing AI jobs faster, keeping costs under control, and maintaining operational agility. An Ethernet fabric that delivers 99% of InfiniBand’s technical performance but can be deployed quickly, managed by existing teams, and scaled more efficiently provides a far superior business result.

Arista’s approach isn’t about merely matching InfiniBand’s latency specs. It’s about building a better business performance platform that harnesses the power of the entire open Ethernet ecosystem. Enterprises that choose Ethernet aren’t compromising on speed—they’re making a strategic move that aligns performance with flexibility and economic sense.

Anatomy of a Lossless Fabric

AI workloads demand networks that go far beyond simply moving packets from point A to point B. To keep massive GPU clusters running efficiently, the network fabric has to deliver predictable performance, extremely low latency, and zero packet loss under heavy load. That’s a tall order for standard Ethernet, traditionally known as a best-effort delivery technology. Yet Ethernet has evolved into a powerful, high-performance, lossless platform thanks to a set of sophisticated, interoperable technologies.

Arista Networks has not only mastered these core standards but has also layered in innovative, AI-focused features that elevate Ethernet into a purpose-built solution for AI data centers. Understanding how this technology stack fits together is crucial for anyone evaluating modern AI network architectures.

Lossless Ethernet

At the heart of a high-performance AI network is the ability to transfer data directly between servers’ memory, avoiding CPU involvement and keeping latency to a minimum. This is made possible through Remote Direct Memory Access (RDMA), which allows a network card to write data straight into another server’s memory. The industry standard for bringing RDMA to Ethernet is RoCEv2 (RDMA over Converged Ethernet version 2). It’s a cornerstone of fast, efficient GPU-to-GPU communication.

But RoCEv2 has a critical requirement: a lossless network.

A single dropped packet can stall an entire AI training run. Achieving truly lossless Ethernet involves multiple technologies working in sync.

Two of the most important are:

Priority Flow Control (PFC): This is like an emergency brake for the network. When congestion threatens to overflow switch buffers, PFC selectively pauses specific traffic types rather than letting packets drop.
Explicit Congestion Notification (ECN): Instead of waiting for buffers to overflow, ECN acts as an early warning system. It flags congestion before it becomes severe, prompting sending devices to slow their rate and reduce the chance of needing PFC pauses at all.

Together, these mechanisms transform Ethernet from an unpredictable transport medium into a reliable, lossless fabric capable of handling the intense demands of AI data flows. Arista’s EOS operating system delivers an optimized implementation of this entire stack, fine-tuned for high-performance AI environments.

Intelligent Traffic Distribution: Arista Cluster Load Balancing (CLB)

Technologies like PFC and ECN ensure lossless delivery, but AI networks face another unique challenge: how to efficiently spread massive data flows across available links. Traditional load balancing methods such as ECMP (Equal-Cost Multi-Path) rely on hashing packet headers like source and destination IP addresses. That works for typical enterprise traffic, which is diverse and high-entropy. But AI traffic is different.

AI workloads often involve a small number of extremely large data flows, known as “elephant flows,” between a limited set of nodes. These flows can easily overwhelm individual network links if they happen to hash to the same path. Meanwhile, other paths might sit idle. This causes congestion, drives up tail latency, and prevents the network from delivering its full performance potential.

To address this, Arista developed Cluster Load Balancing (CLB), an RDMA-aware, intelligent load balancing solution designed specifically for AI fabrics. Unlike simple header hashing, CLB uses RDMA queue pairs as a source of entropy, allowing it to identify and split flows more effectively.

Most importantly, CLB operates with a global view of the entire fabric. It optimizes traffic in both the leaf-to-spine and spine-to-leaf directions, distributing traffic evenly across all paths in the network. This global optimization helps eliminate hotspots, improves utilization, and directly tackles the problem of tail latency in large AI clusters. With CLB, Arista moves beyond simply providing a reliable network “pipe” to engineering an application-aware, AI-optimized fabric.

Complexity and the Need for Expertise

Deploying a high-performance, lossless Ethernet fabric for AI isn’t plug-and-play. It requires meticulous tuning of quality-of-service (QoS) policies, buffer thresholds, DSCP markings, and congestion management settings that can vary from one environment to another. Detailed multi-page deployment guides exist for a reason, and tools like Arista’s PFC Watchdog are essential to guard against issues like pause storms, which can disrupt traffic if PFC isn’t carefully managed.

This complexity underscores why having a specialized partner, like Intelligent Visibility, is crucial. Designing, fine-tuning, and validating a lossless Ethernet fabric for AI workloads is a deep engineering challenge. Working with experts ensures that the network not only meets technical specifications but delivers predictable, high-performance results that maximize the return on costly GPU investments.

Table 2: Arista's Lossless Ethernet Technology Stack

Arista's Blueprint for AI Networks: A Unified Platform

Instead of piecing together separate products, Arista delivers a unified platform that integrates hardware, software, and management into a cohesive system. This strategy, embodied in the Etherlink™ AI Portfolio, offers a clear and scalable blueprint for building AI centers of any size, whether it’s a small research cluster or a hyperscale deployment housing more than 100,000 accelerators. The portfolio is built on open standards and is designed to align seamlessly with emerging Ultra Ethernet Consortium (UEC) specifications, ensuring that investments made today remain relevant well into the future.

In the AI era, networks aren’t just fast—they’re unified, intelligent, and built to scale from ten GPUs to a hundred thousand without missing a beat.

Hardware: AI-Ready Switching

Arista’s hardware lineup is purpose-built for each layer of a modern AI network. The goal is simple: deliver the high bandwidth, low latency, and deep buffering needed to keep data moving efficiently across massive GPU infrastructures.

Leaf Switches for GPU Pods — 7060X6 Series

At the leaf layer, the Arista 7060X6 series connects directly to the network interface cards (NICs) of GPU accelerators. This switch aggregates traffic from GPU pods before sending it upstream to the spine.

Role and Function: In a leaf-spine topology, the 7060X6 handles the crucial first hop between densely packed GPU servers and the broader network. Its high port density and flexible speeds make it ideal for scaling GPU clusters efficiently.
Key Specifications: The 7060X6 series offers up to 64 ports of 800G or 128 ports of 400G in a compact 2RU chassis. It delivers consistent line-rate performance with latency as low as 700 nanoseconds. The switch includes a large, 165 MB fully shared packet buffer to absorb microbursts of GPU traffic without dropping packets.
AI Optimization: Designed specifically for AI workloads, the 7060X6 includes hardware support for critical congestion control and load-balancing technologies like RoCEv2 and DCQCN, ensuring reliable, high-speed communication between GPUs.

Spine Switches for Cluster Backbones — 7800R4 Series

The Arista 7800R4 series forms the backbone of large-scale AI networks. It’s engineered to deliver massive, non-blocking capacity and ultra-deep buffering for seamless traffic flow across thousands of GPUs.

Role and Function: Acting as the central interconnect for leaf switches, the 7800R4 enables a single-hop fabric for clusters spanning thousands of GPUs. This simplifies architecture and minimizes latency.
Key Specifications: The modular 7800R4 scales up to 460 Tbps of system throughput and offers up to 576 wire-speed 800G ports in one chassis. Its standout feature is its ultra-deep buffers, with up to 32 GB of memory per line card, which helps handle extreme incast congestion without dropping packets.
AI Optimization: Unlike conventional switches, the 7800R4 uses a fully scheduled, cell-based fabric with Virtual Output Queuing (VOQ). This architecture segments packets into uniform cells and distributes them across all fabric links, preventing head-of-line blocking and ensuring even, predictable traffic flow. It also supports advanced AI-focused features like Cluster Load Balancing (CLB), critical for large AI workloads.

Distributed Spine for Massive Scale-Out — 7700R4 Distributed Etherlink Switch (DES)

For the largest AI deployments in the world, Arista developed the 7700R4 DES architecture. It’s designed to transcend the physical limits of a single chassis and deliver unprecedented scale.

Role and Function: The 7700R4 DES is built for hyperscale clusters, scaling beyond 27,000 800G ports. It consists of physically distributed leaf and spine units that operate as one logical switch, bringing massive scale without operational complexity.
Key Architecture: The DES architecture maintains the same core principles as the 7800R4, including a fully scheduled, lossless, VOQ-based fabric. However, it extends this capability into a distributed topology, enabling single-hop efficiency across multiple racks. This eliminates the need for complicated inter-switch tuning that often plagues traditional multi-tier designs.
AI Optimization: The 7700R4 DES is topology-agnostic and ready for UEC specifications, making it the go-to platform for hyperscalers like Meta that demand extreme scalability without compromising performance or manageability.

Software Intelligence Layer: Arista EOS® and CloudVision®

Arista’s competitive edge doesn’t stop at hardware. The company’s unified software stack provides operational consistency, deep visibility, and intelligent automation across the entire AI networking environment.

Arista EOS: A Single Operating System Across All Platforms

Arista EOS (Extensible Operating System) runs across every Arista switch, eliminating the operational silos that plague competitors with multiple OS variants. This single-OS architecture ensures consistent feature sets, command-line interface (CLI), and APIs throughout the network. Automation scripts, tools, and operational expertise developed for one part of the infrastructure can be reused seamlessly across all devices, from leaf switches to massive spines.

For AI networking, EOS provides robust implementations of technologies like RoCEv2, DCQCN, PFC/ECN, and advanced load balancing with CLB. This consistency is crucial for maintaining a stable, lossless fabric as AI environments scale and evolve.

Arista CloudVision: Centralized Management and Observability

CloudVision is the nerve center for managing and automating Arista networks. It transforms network operations from a reactive, device-by-device CLI approach to a proactive, centralized, software-driven model. This shift is particularly important in the high-stakes world of AI networking.

AI-Driven Automation: CloudVision Studios enables engineers to deploy complex configurations with simple, customizable workflows. Pre-validated templates based on Arista Validated Designs (AVD) reduce the chance of human error during deployment and growth phases.
AI-Centric Observability (CV UNO): Traditional network monitoring can’t see the full picture in AI environments. Arista’s CloudVision Universal Network Observability (CV UNO) bridges this gap. It creates a centralized Network Data Lake that captures streaming telemetry from switches, server NICs, and AI job schedulers.

CV UNO correlates network-level events, like ECN marks and PFC pauses, with application-level metrics such as Job Completion Time (JCT). Through the Arista Autonomous Virtual Assist (AVA) AI engine, the system analyzes this data to detect anomalies and suggest precise corrective actions. When an AI workload slows down, operators can pinpoint the cause, whether it’s a congested link, a misconfigured NIC, or a server issue, avoiding costly finger-pointing across teams.

Table 3: Arista's AI-Ready Switching At-a-Glance

From Theory to Practice: Real-World Deployments at Scale

The value of standards-based, lossless Ethernet for AI networking isn’t just theoretical. It’s been tested and proven in some of the largest, most demanding AI environments in the world. High-profile deployments by companies like Meta and Oracle Cloud Infrastructure (OCI) provide strong, real-world evidence that Arista’s architecture works, even at hyperscale, and removes the guesswork for enterprises considering this technology.

Hyperscale players have shown the world that Ethernet can match or surpass proprietary fabrics for AI — and that’s a game changer for enterprises ready to scale.

Case Study: Meta’s 24,000-GPU GenAI Cluster

Meta set a new bar for AI networking when it built two parallel 24,576-GPU clusters for training next-generation AI models like Llama 3. One cluster used InfiniBand, while the other ran on a RoCE-based Ethernet fabric built on Arista switches. This wasn’t just a lab test; it was a real, production-grade comparison of the two technologies at massive scale.

The Arista Solution: Meta’s Ethernet cluster used Arista 7800 series switches for the high-radix spine, combined with OCP-compliant Wedge400 and Minipack2 switches at the leaf layer. This architecture created a robust 400 Gbps RoCE fabric to support the intense traffic from over 24,000 NVIDIA H100 GPUs. Meta has since scaled further, deploying Arista’s 7700R4 Distributed Etherlink Switch (DES) to handle even larger clusters.

The Outcome: Meta publicly confirmed that the Ethernet-based cluster handled its most demanding generative AI workloads without bottlenecks, delivering performance equal to the InfiniBand cluster. The success came from more than just hardware. Meta highlighted critical optimizations, including:

Topology-aware job scheduling to minimize traffic crossing higher tiers of the fabric.
Routing and transport tuning, including adjustments to NVIDIA’s NCCL for efficient inter-GPU communication.

These engineering efforts took the Ethernet cluster from inconsistent utilization levels as low as 10 percent to a steady 90 percent or higher, matching the efficiency of smaller clusters. The bottom line: standards-based Ethernet isn’t just viable, it’s proven to deliver at the very highest levels of scale.

Meta’s experiment answered the ultimate question: Ethernet works for AI at a scale few enterprises will ever touch.

Case Study: Oracle Cloud Infrastructure (OCI)

While Meta’s story proves Ethernet’s raw performance, Oracle Cloud Infrastructure shows how it delivers value for enterprises running multi-tenant AI services.

The Challenge: OCI needed to ensure reliable, high-performance networking for diverse machine learning workloads across a massive AI infrastructure. Avoiding traffic contention and maximizing throughput are critical to delivering competitive cloud services.

The Arista Solution: OCI has chosen Arista switches as the backbone for its AI training networks. In a significant public statement, Jag Brar, Vice President and Distinguished Engineer at OCI, singled out Arista’s Cluster Load Balancing (CLB) as a key differentiator:

“As Oracle continues to grow its AI infrastructure leveraging Arista switches, we see a need for advanced load balancing techniques to help avoid flow contentions and increase throughput in ML networks. Arista’s Cluster Load Balancing feature helps do that.”

This endorsement goes beyond praising Ethernet’s general capabilities. It highlights how Arista’s unique innovations, like CLB, directly solve real-world performance issues in enterprise AI deployments.

The Role of Expertise

Both Meta’s and Oracle’s stories reveal an essential truth: deploying high-performance AI networks isn’t plug-and-play. It demands careful co-design and optimization across the entire stack, from hardware to software to AI workloads themselves. Even world-class engineering teams like Meta’s needed significant fine-tuning to reach peak efficiency.

For enterprises without armies of in-house network engineers, this is where expert partners like Intelligent Visibility become critical. As a managed services provider, Intelligent Visibility bridges the gap, delivering the design, deployment, and tuning expertise needed to transform Arista’s powerful hardware and software into real business results for AI.

The Future is Open: Arista and the Ultra Ethernet Consortium (UEC)

The success of lossless Ethernet for AI proves that open networking can handle the world’s most demanding workloads. But today’s solutions, built on RoCEv2, PFC, and ECN, are essentially retrofits on top of a protocol never designed for high-performance computing. That creates complexity and requires deep tuning to work reliably at scale. To solve this for the long term, the industry’s biggest players have joined forces under the Ultra Ethernet Consortium (UEC), aiming to define a new era of Ethernet built specifically for AI and HPC.

“UEC is the industry’s blueprint for Ethernet that’s purpose-built for AI — combining hyperscale performance with the openness enterprises demand.”

The Vision Behind UEC

The Ultra Ethernet Consortium was founded with a clear goal: build an open, interoperable Ethernet standard that combines the performance of supercomputing fabrics with Ethernet’s ubiquity and cost efficiency. Rather than patching old protocols, UEC is redefining how data moves through AI clusters from the ground up.

Key goals for UEC include:

Modern RDMA: Developing smarter, more efficient transport protocols to replace legacy RoCE limitations.
Open Standards: Ensuring a multi-vendor ecosystem that prevents vendor lock-in and fosters rapid innovation.
Massive Scalability: Designing networks that can scale smoothly to millions of endpoints while remaining operationally manageable.

What’s New in UEC 1.0

The first UEC specification introduces several major technical innovations designed to overcome current Ethernet limitations for AI workloads:

Ultra Ethernet Transport (UET): A brand-new transport layer designed as a more efficient, reliable alternative to RoCE. It promises streamlined data transfers with built-in flow control.
Advanced Congestion Control (UEC-CC): A smarter congestion system that’s more responsive and far easier to tune than traditional DCQCN, simplifying large-scale network operations.
Packet Spraying and Flexible Multipathing: Instead of relying on flow-based hashing, UET can spread packets from a single flow across all available paths, maximizing fabric bandwidth and preventing network hotspots.
Link-Level Retry (LLR): In a significant shift, UEC pushes packet retransmissions down to the link level. Lost packets can be resent immediately by the switch rather than waiting for end-to-end timeouts. This sharply reduces tail latency and improves resilience during heavy traffic.

Link-Level Retry changes the game by fixing packet loss instantly at the switch, slashing latency and keeping AI jobs running at peak speed.

Arista’s Leadership and UEC-Ready Strategy

Arista isn’t just following the UEC story; it’s helping write it. As a founding member of the consortium alongside Microsoft, Meta, AMD, Broadcom, and Intel, Arista is deeply involved in designing the next generation of open, high-performance Ethernet.

Arista has also built its Etherlink™ portfolio to be ready for the future. Its AI-optimized platforms like the 7800R4 and the 7700R4 DES are designed to be forward-compatible with UEC standards. This means customers investing in Arista today get high-performance Ethernet solutions capable of handling current AI workloads — with the confidence that their infrastructure can evolve to support UEC when compliant hardware and silicon become available, expected around late 2025 or early 2026.

This forward-compatible approach tackles one of the biggest customer concerns in fast-moving markets: the risk of buying technology that might soon be outdated. Arista’s strategy provides a practical answer for organizations wrestling with the question, “Should we wait for UEC?” Instead, customers can build AI infrastructure now, on proven hardware, knowing they’ll have a seamless upgrade path to the next generation of Ethernet once it arrives.

Arista’s UEC-ready platforms let enterprises move fast today without locking themselves out of tomorrow.

Maximizing ROI: The Role of the Managed Network Services Partner

A purpose-built, lossless Ethernet fabric is the ideal foundation for enterprise AI, and Arista Networks offers the most advanced and proven platform to build it. Yet, as the Meta case study and technical details make clear, deploying and operating a high-performance AI network isn’t simple. The technology’s power and flexibility come with complexity that’s often beyond the reach of typical enterprise IT teams. This creates a gap between owning the right hardware and truly unlocking its value. That’s where a skilled managed service partner (MSP) like Intelligent Visibility makes the difference.

The right MSP transforms powerful hardware into business outcomes, turning AI networking from a science project into a competitive advantage.

The Challenge: Complexity at Every Layer

Running a lossless AI fabric isn’t a job that can be set up once and forgotten. It demands specialized expertise across several new domains for most enterprise teams:

Precision Fabric Tuning: Building a stable RoCEv2 network takes careful configuration of QoS policies, DSCP values, traffic classes, and buffer allocations on both leaf and spine switches. These settings aren’t generic—they must be tuned for the cluster’s size, architecture, and AI workload specifics.
Sophisticated Congestion Management: Preventing congestion isn’t just about setting thresholds. It involves fine-tuning ECN to react quickly without triggering excessive slowdowns, and configuring PFC in a way that prevents packet loss without causing pause storms. Tools like PFC Watchdog add essential safety nets but require deep understanding to use properly.
Advanced Automation and Observability: Using Arista CloudVision to its full potential means more than clicking through a user interface. It requires automation skills, scripting with APIs, integrating with systems like ITSM platforms, and making sense of high-frequency telemetry data to extract meaningful, actionable insights.
Cross-Domain Troubleshooting: Performance issues in AI clusters can stem from the network, server NICs, the host operating system, or even the AI applications themselves. Pinpointing the root cause demands the ability to connect application metrics like Job Completion Time (JCT) with low-level network details such as PFC pauses and RDMA errors.

In AI networking, troubleshooting means finding a needle in a haystack—while the haystack is moving at terabits per second.

The Solution: Intelligent Visibility’s Expert Services

As a specialized Arista partner, Intelligent Visibility delivers professional and managed services designed precisely for these challenges. Their mission is to help enterprises get the most value out of their AI networking investments.

Accelerating Time to Value: Rather than spending months trying to learn and fine-tune complex systems, enterprises can rely on Intelligent Visibility’s experience. Their team leads architecture, design, and integration, ensuring AI networks are deployed right the first time and perform at their peak from day one.
De-risking Deployment: Intelligent Visibility brings deep knowledge of RoCEv2 tuning, optimal buffer sizing, and integration with data center fabrics like EVPN/VXLAN. This helps enterprises avoid missteps that could lead to performance issues or expensive rework.
Enabling Advanced Automation: Intelligent Visibility helps clients tap into the power of Arista CloudVision and Arista Validated Designs (AVD). They design and build automation solutions that reduce manual work, cut down on operational errors, and make the network more agile and resilient.
Providing Ongoing Optimization with Aegis Managed Services: AI networks are dynamic environments where conditions change daily. Intelligent Visibility’s Aegis Managed Services provide a co-managed model that keeps networks running optimally long after initial deployment.

The Aegis Managed Services Advantage

Aegis PM (Performance Monitoring): Continuously ingests and analyzes real-time telemetry from CloudVision, using anomaly detection to flag potential issues before they affect AI workloads.
Aegis IR (Incident Response): Provides rapid root cause analysis and expert guidance for resolving performance issues, dramatically cutting time to resolution and protecting critical AI jobs.
Aegis CM (Configuration Management): Maintains a secure, compliant network environment with automated drift detection, validation against best practices, and managed patching and upgrades.

This co-managed model allows enterprise IT teams to stay in control of high-level strategy and architecture while offloading specialized, day-to-day network operations to a team of experts. That frees internal resources to focus on higher-value initiatives aligned with business goals.

The Shift from Managing Devices to Managing Outcomes

The value proposition of an MSP in the AI era is no longer just about keeping switches online. It’s about achieving tangible business outcomes, like consistently low JCT and maximizing GPU utilization. The complexity of AI networking isn’t a flaw; it’s the reason specialized expertise matters. An expert MSP like Intelligent Visibility bridges the gap between Arista’s advanced technology and the real-world results enterprises need from their AI investments.

In AI networking, success is measured not by uptime alone, but by how quickly you turn data into insights—and profits.

Conclusion: Building AI Networks That Deliver

AI isn’t just reshaping software. It’s redefining the demands placed on data center infrastructure. The network has moved from being a background utility to a central driver of AI performance. For any organization aiming to scale AI, network architecture is now a strategic investment that determines whether high-cost GPUs generate insights—or sit idle.

In AI, the network has become part of the compute fabric itself. It’s not just about moving data. It’s about enabling the speed and scale that turn models into real results.

Why Traditional Networks Fall Short

AI workloads generate massive, parallel data flows and require tightly controlled latency. Legacy network designs weren’t built for this. Trying to run AI on old architectures leads to congestion, longer job times, and underused GPU clusters that waste both time and money.

Why Open Ethernet Makes Sense

While InfiniBand has been one of the go-to solutions in traditional high-performance computing, open, standards-based Ethernet has caught up—and often surpassed it. Technologies like RoCEv2, PFC, and ECN now let Ethernet deliver the same performance for demanding AI workloads, as proven in deployments like Meta’s.

But Ethernet brings more than just technical parity:
    •    It’s part of a competitive, multi-vendor ecosystem that fosters innovation and keeps costs down.
    •    It integrates cleanly with existing enterprise networks.
    •    It scales seamlessly from small AI clusters to massive hyperscale environments.
    •    It provides a better long-term economic model for enterprises balancing performance and cost.

Why Arista Leads

Arista Networks offers the industry’s most complete platform for building high-performance AI networks. From leaf switches like the 7060X6 to the massive-scale 7800R4 spine and 7700R4 DES, Arista’s hardware is built for the demands of AI traffic. This entire portfolio runs on EOS, Arista’s single, unified operating system, and is managed through CloudVision for visibility and automation.

Arista’s solutions aren’t just theoretical—they’re proven in production at the largest scales. And their leadership in the Ultra Ethernet Consortium ensures customers are ready for what’s next in AI networking.

And Arista's industry-leading quality and support, possible because of the fundamental architectural superiority of EOS, and their leadership in high-speed networking demonstrate their commitment to their customers, partners, and the networking industry as a whole.

Strategic Recommendations

Modern, lossless Ethernet fabrics are essential for AI at scale. But owning the right hardware is just the start. Designing, deploying, and operating AI networks demands specialized expertise. Mistakes can mean stalled projects, high costs, and unrealized performance.

Enterprises don’t have to navigate this alone. A partner like Intelligent Visibility bridges the gap between advanced hardware and real-world outcomes. With deep experience in Arista platforms and AI networking, Intelligent Visibility helps organizations:
    •    Architect and deploy AI networks right the first time.
    •    Tune complex fabrics for peak performance.
    •    Continuously monitor and optimize networks as AI workloads evolve.

Investing in Arista’s technology—and pairing it with the expertise of a focused partner—ensures that your AI network isn’t just running, but delivering meaningful business results.

If you’re planning your AI infrastructure journey, connect with our team to explore how we can help you build it with confidence.