How AI is Changing Enterprise Networking and the Strengths of Edgecore Networking Switches

AI Inference: AI Inferencing is changing both the volume and behavior of how data needs to be shared across the enterprise network.

AI Inference Traffic: AI Inference differs from traditional data flow in that it requires frequently massive data uploads combined with continuous and rapid feedback loops.

Data Demand Growth: AI Inferencing is putting continued pressure on network bandwidth. Gartner reports that enterprises experience a 30-50% increase in data traffic within 2 years of scaling AI implementations.

AI Inference and the Enterprise

As corporations transition from AI training to Inferencing which involves running data through trained AI models to generate real-time business outcomes unlocking new levels of productivity, AI inferencing is fundamentally restructuring corporate network architecture.   

How AI in Enterprises is Changing Networking

The continued evolution of AI workloads is placing unprecedented demand on the network infrastructure. Unlike traditional applications, AI models require the constant movement of massive datasets between local users, cloud-based GPUs, and central data centers  fundamentally changing the flow of network traffic. The modern infrastructure transitions from a North-South top down data flow to a dynamic east-west flow that requires low latency, maximum throughput, and zero bottlenecks in order to maximize AI compute.

In the world of AI, modern servers talk to each other in an east-west traffic flow more than they talk to outside users in a north-south flow. Traditional Three-Tier Networks are prone to bottlenecks as they scale to support more traffic from larger and more frequent workloads. Additionally, they have increased latency as data moves or hops through the multiple layers in an attempt to move east to west which not only impacts performance but also increases the complexity of managing the data flow across the network. All this ultimately results in under-utilized AI compute resources, slower response, and increased costs.

To solve this problem, modern networks utilize a Spine-Leaf architecture which consists of two-layers, a Spine layer made up of switches that route traffic between the Leaf layer which is connected to the servers and end-points. This topology allows each leaf switch to be connected to each spine switch to support the efficient flow of east-west traffic, reduce latency, and provide better scalability which are all essential to maximize AI performance and the demands AI places on the network.

Advantages of a Spine-Leaf Architecture

Scalability: Spine-leaf designs can scale horizontally by adding more switches to both the Spine and Leaf layers. This allows for the consistent performance as the network grows

Low Latency: As traffic flows east to west, there are fewer hops at each layer which reduces latency.

Resilience: With its fully meshed topology, spine-leaf architecture offers built-in redundancy, enhancing reliability.

Performance: The spine-leaf design ensures consistent performance across large networks, making it ideal to support AI applications in data centers, enterprises, universities, energy, life science, finance, government and other markets.

Edgecore Networks offers a comprehensive portfolio of high-performance, open-networking switch solutions specifically designed to handle the massive bandwidth, low latency, and non-blocking traffic requirements of modern AI/ML enterprise data centers.

Built around standard Ethernet topologies, disaggregated hardware, and open network operating systems (NOS) like SONiC, Edgecore’s AI network stack spans from high-density spine/leaf fabric platforms to dedicated management switches.

Key Hardware Platforms for AI Workloads

Edgecore’s flagship AI platforms (the AIS series) utilize industry-leading silicon, including Broadcom’s Tomahawk line, to optimize Job Completion Time (JCT) for large language model (LLM) training and AI inference clusters:

Architectural & Technical Features for Enterprise AI

Edgecore’s AI switch solutions address the unique network bottlenecks associated with GPU-to-GPU interconnects and massive collective communication (e.g., AllReduce algorithms):

• Advanced Congestion Management: Supports RoCEv2 (RDMA over Converged Ethernet), Explicit Congestion Notification (ECN), Priority Flow Control (PFC), and Fast CNP to prevent packet drops and maintain lossless transmission across the cluster.
• Ultra-Low Latency & High Radix: Devices like the AIS800 deliver sub-microsecond latency and up to 512 logical ports per chip, enabling larger, flat 2-tier topologies that reduce overall network hop count.
• Hardware-Based Link Failover: Built-in hardware resiliency ensures near-instantaneous rerouting during link degradation or failures, avoiding GPU idle time during long training jobs.
• Deep Telemetry & Visibility: Features integrated In-band Network Telemetry (INT) to monitor buffer utilization, packet drops, and traffic flows in real time.
• Open Ecosystem & NOS Choice: As a pioneer in open networking, Edgecore switches support disaggregated architectures using SONiC (Software for Open Networking in the Cloud), ONL, or commercial partner operating systems, preventing vendor lock-in.

Switch Family Specification Matrix

Edgecore comparison of high-performance AI switch family series and management units.