The Rise of Ethernet-Based AI Networks: Replacing Traditional Telecom Infrastructure

Telecoms networks are undergoing a fundamental transformation. AI-driven applications, ranging from large-scale language models to real-time analytics,  offer unprecedented high-speed and low-latency connectivity. But traditional telecoms infrastructure, which often relies on proprietary networking hardware, is struggling to keep up with the computational intensity of AI workloads. As a result, ethernet-based AI networks are emerging as the go-to solution for hyperscale data centres, cloud providers and telecoms operators looking to modernise and better manage their network infrastructure.

Far from  just a technical upgrade, this shift represents a paradigm shift in how AI applications interact with network infrastructure, paving the way for a new era of AI-powered telecoms networks. In this article, we will explore the factors driving the transition to ethernet-based AI networking, its benefits, real-world use cases and what the future may hold for this evolving technology.

Legacy telecoms networks were originally designed to handle voice and broadband services, which falls short of the bandwidth and low latency requirements needed to fulfil AI workloads. Key limitations of legacy networks include:

• Limited Scalability: Proprietary networking solutions restrict the ability to scale AI-driven operations effectively;
• High Latency: Latency shortfalls with traditional telecoms networks can result in delays that adversely affect real-time AI applications;
• Energy Inefficiency: Legacy telecoms hardware is often unable to optimise power consumption, which is an essential requirement for AI workloads;
• Rigid Architectures: Closed hardware-defined networks lack the flexibility to manage AI-driven applications.

Ethernet-based AI networks represent a high-performance, cost-efficient and scalable solution that can address these issues and engage in AI processing at scale.

High Bandwidth, Low Latency and Scalability

Ethernet-based AI networks combine high-speed data transfer with ultra-low latency, facilitating faster training and deployment of  AI models. Unlike legacy telecoms networks, which often suffer from bandwidth bottlenecks, ethernet’s scalability supports the significant interconnectivity needs of AI workloads.

The rise in ethernet speeds,  from 10Gbps to 100Gbps, 800Gbps and beyond, enables AI models to process data in real time without suffering from performance degradation. This is particularly crucial for applications such as:

• Large-scale AI training models (e.g., ChatGPT, DALL·E, and LLaMA models);
• Autonomous driving networks that require rapid sensor-to-AI processing;
• AI-powered financial trading systems that rely on rapid millisecond-scale decision-making.
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Hyperscalers Driving the Adoption of AI Ethernet Networks

Cloud providers and hyperscalers are leading the transition toward ethernet-based AI networking, as they seek to scale AI-driven operations, optimise power consumption and manage their AI workloads more effectively. Companies such as Google, AWS and Microsoft have invested billions of dollars to build ethernet-driven AI data centres. Some of the key functionality within these centres include:

• AWS Elastic Fabric Adapter (EFA): AWS uses ethernet-based AI networking to train its machine learning models at low-latency within its cloud infrastructure;
• Google's AI Supercomputer: Google’s Tensor Processing Units (TPUs) are connected via high-bandwidth ethernet networking to optimise AI workloads;
• Microsoft’s AI Datacentres: Microsoft Azure uses AI-driven ethernet networking to enhance connectivity for large-scale AI deployments.

These investments highlight how ethernet capability is reducing costs and improving performance for AI-driven operations.

Given the power requirements of AI-driven data centres, energy efficiency is a top priority. Ethernet-based AI networks deliver on these energy needs in the following ways:

• Power Consumption: AI workloads require efficient power distribution. Ethernet solutions improve energy efficiency by allowing data centres to allocate resources where needed;
• Cooling Technologies: Advanced liquid cooling and AI-powered cooling algorithms work seamlessly with ethernet networking to reduce data centre heat generation;
• Sustainable AI Operations: Data centres were responsible for around 2% of global electricity usage in 2022. This is set to double by 2026,  pushing operators to seek sustainable solutions such as Ethernet-based networks.

The transition to ethernet-based AI networks is the beginning of a new era of telecoms network management. Some exciting developments on the horizon include:

AI-Native Network Architectures

Ethernet capability is evolving to support AI workloads, elevating the role of networks beyond that of an infrastructure layer, to a system that actively optimises AI performance through intelligent routing and data processing.

The Convergence of AI, 6G and Edge Computing

With work on 6G technology already  underway, AI and network management will become increasingly intertwined. AI-driven network automation, real-time AI inference at the edge and intelligent traffic management will redefine connectivity in several use cases, including:

• Smart cities and AI-driven IoT networks;
• Ultra-low latency applications in healthcare, such as AI-assisted robotic surgeries;
• AI-powered augmented reality and metaverse applications.

Open Ethernet Networks and Standardisation

Open networking standards for ethernet-based AI networks are currently being  developed to accelerate AI adoption. The push for open-source AI networking solutions has several advantages:

• Greater interoperability across AI data centres;
• Lower costs through the removal of vendor lock-in;
• A community-driven approach to optimising AI connectivity.