Network Infrastructure for AI Workloads: Building for Speed, Scale and Intelligence

Why Networks Are the Backbone of AI

Artificial Intelligence has become  a daily business enabler and competitive differentiator. But while AI advancements are largely associated with compute power and model sophistication, one critical element often gets overlooked: network infrastructure. As AI models grow larger and more complex, and as enterprises increasingly operationalise AI in real-time, the networks behind these workloads must evolve to deliver the necessary speed, scale and intelligence. Without high-performance networking and ultra-low latency, even the most powerful compute clusters are limited.

Defining the Foundations

AI workloads place uniquely intense demands on infrastructure, particularly when it comes to data movement. Unlike traditional applications that primarily rely on north-south traffic, which is the traffic between users and data centres, AI workloads generate vast amounts of east-west traffic. This is the communication between nodes within a data centre. This east-west communication is especially critical during distributed model training, where vast datasets are split and processed across multiple GPUs.

Speed and low latency are key. When multiple GPUs or accelerators are working in parallel, even small amounts of delay or network jitter can have a compounding impact on performance. High-bandwidth, low-latency networking, such as InfiniBand or Ethernet with RDMA over Converged Ethernet (RoCE), is becoming a requirement, not a luxury. Modern AI clusters now rely on non-blocking network fabrics with speeds of 400G or even 800G to maintain seamless communication between compute nodes.

Real‑World Stats: AI Adoption & User Trends

​​AI adoption has surged dramatically across both corporate and individual landscapes, reshaping how work is done and decisions are made. Today, 78% of companies globally report using AI in at least one area of their business—a significant rise from just 55% in 2023. Among large enterprises, adoption is nearly ubiquitous, with 99% of Fortune 500 companies embracing AI technologies. This shift is mirrored in the modern workforce, where knowledge workers now incorporate AI into their daily routines. 

On an individual level, tools like ChatGPT have become deeply embedded in everyday workflows, boasting 14.3 million daily users and over 100 million active users weekly. Notably, Gen Z professionals are leading this transformation, with around 80% relying on AI for more than half of their tasks. 

These figures underscore the growing reliance on AI across industries and demographics, driving an urgent need for network infrastructure that can match the speed, scale, and intelligence of modern workloads.

To support this AI-driven future, network infrastructure must go beyond performance alone. It must be intelligent, scalable and efficient. Let’s explore a few emerging innovations and principles that will define next-generation AI networking:

1. Silicon Photonics and Optical Interconnects

AI clusters require incredible amounts of bandwidth, and traditional copper-based networking is approaching its limits. Silicon photonics is emerging as a breakthrough technology, allowing data to be transmitted via light rather than electrons. NVIDIA, for example, has already begun incorporating on-chip photonics into its networking gear, targeting speeds of up to 1.6Tbps per port. These advances will not only accelerate data movement but also reduce power consumption significantly.

2. Disaggregated Architectures and DPUs

Next-gen infrastructure will rely more heavily on disaggregated components, separating compute, storage and networking for improved flexibility and performance. Data Processing Units (DPUs) are becoming a key part of this architecture, offloading networking and security tasks from CPUs and optimising data flow for AI-specific workloads.

3. Edge-to-Core Integration

As enterprises deploy AI across edge environments—retail stores, factories, and vehicles—the network must be capable of supporting ultra-low latency communications between edge devices and centralised training clusters. Gartner predicts that 75% of enterprise data will be generated at the edge by 2025, highlighting the need for edge-to-core networking that is both resilient and fast.

4. Automation and Resilience

Network automation will be crucial for managing complexity at scale. With the volume of AI traffic and interconnected nodes growing rapidly, human error becomes a major risk. Automated orchestration, monitoring, and fault resolution are essential for ensuring continuous performance and uptime.

Strategic Implications for BSO Clients

For BSO clients building or expanding AI infrastructure, the message is clear: investing in scalable, intelligent, high-throughput networks will enable organisations to stay ahead of the curve and to:

• Invest in High-Speed, RDMA-Capable Fabrics
  Clients building AI clusters should prioritise InfiniBand or Ethernet + RoCE fabrics with fat-tree or Clos topologies, with 400G+ uplinks to scale with GPU densities.

• Adopt Photonic & DPU Acceleration
  Encourage early pilots in silicon photonics and DPUs to reduce CPU-based routing load and embrace future-ready connectivity.

• Automate Deployment at Scale
  Network orchestration tools must be integrated from day one to ensure predictable performance and avoid human-induced downtime.

• Balance Core and Edge Infrastructure
  A hybrid approach - distributed inference at the edge, centralized training - yields the best performance, while addressing latency and data sovereignty.

• Sustainability Beyond Performance
  Photonics promise ~50% power savings. Efficient networks reduce both operational cost and environmental impact.

Building Intelligent Infrastructure for an AI-Driven World

AI is transforming how businesses operate, but it cannot thrive without the right foundation. As adoption grows and workloads scale, the demand for agile, high-performance networking will only intensify. From silicon photonics and DPUs to edge integration and automation, the future of AI-ready infrastructure lies in intelligent design and strategic investment.

At BSO, we understand the complexity and urgency of building network infrastructure that enables innovation. By architecting for speed, scale and intelligence, organisations can position themselves at the forefront of the AI revolution.

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