Networking for AI: Transformation beyond the chip

Posted on
March 6, 2026

Data Center IT Infrastructure

Companies are recognizing AI as a competitive differentiator and use cases are proliferating. Powerful GPUs have captured imaginations and wallet share in the race to process extraordinary amounts of data faster. The technology is sensational, but what about the other hardware that makes AI computing possible?

If compute is the brain of the digital world, networking is the central nervous system — and it’s undergoing a significant transformation of its own. Welcome to the era of high-performance AI networking.

Why does AI require specialized networking?

GPUs are very expensive, especially in the quantities required for large AI workloads. High performance, rack scale supercomputer platforms with 70-plus GPUs on board drive millions in capex. Enterprises and cloud providers investing heavily in advanced chipsets don’t want network bottlenecks to slow them down. What was once fine for business application data traveling at 25 gigabits per second (Gbps) is woefully inadequate at speeds approaching 1,600 Gbps (1.6 terabits per second), as is common during AI training. A high bandwidth, low latency AI network infrastructure is non-negotiable.

Line chart showing data transfer rates for common LAN standards from 1980 to 2025, with points gradually increasing over time and sharply rising after 2010 to reach multi‑gigabit speeds.

LAN data transfer rates have grown exponentially over the past decade, and some standards are expected to reach speeds of up to 25.6 Tbps in 2026, reinforcing the need for data center operators to ensure that they’re using the latest technologies to future-proof their sites.

The AI networking topology: Scaling up, out, and across

Reducing network bottlenecks during AI data transfer between compute nodes has its challenges. Networking for AI is a three-prong problem: scaling up within the rack, scaling out between racks, and scaling across data center facilities when distributed AI networking is required due to space or power constraints.

Scaling up

The challenge in scaling up rests largely on connecting all of the GPUs in a rack with low latency, high bandwidth connections. Copper cable intra-rack networks are bandwidth constrained, and service providers are increasingly migrating these connections to fiber to minimize traffic jams and data loss, the parameters of which will evolve as GPUs do. Specialized AI interconnects may be required.

Blue line-art illustration of a gear with arrows pointing up, down, left, and right to represent multidirectional operations or system integration.

Scaling out

When hundreds of racks are connected in the data center, the challenge becomes managing congestion as multiple data flows compete for the same bandwidth. Advanced protocols enable least-point fabrics to optimize east-west AI traffic flow with networking switches, providing cost-optimized, high capacity, low latency links between GPUs.

Scaling across

When large AI workloads cannot be handled within a single data center, scale-across switches unify GPUs between multiple facilities so they can function as one. A high performance, geographically distributed AI fiber network infrastructure overcomes distance and data encryption challenges.

What do you need to know when designing low latency networking for real-time AI inference workloads?

Engineers must balance an array of parameters to optimize AI networking architecture, performance, scalability, and cost, including:

Bandwidth per GPU during real workloads
How fabrics handle synchronized GPU traffic spikes
Microsecond latency budgets from hop to hop
Performance differences across topologies such as Clos, FatTree, and Dragonfly
Limits of copper vs. fiber at high speeds and varying distances
Maturity of co-packaged optics and silicon photonics
Thermal and power implications of switch deployments

Impact of link failures during active model training
Operating system behavior and stability at hyperscale
Optical assembly tolerances and loss budgets
Switch silicon tradeoffs across vendors
GPU utilization relative to network stalls
Common failure modes in dense AI network fabrics
Shoreline bandwidth density (data transmission capacity per unit length along a component edge)
Energy efficiency (pJ/bit)

How do you deliver high-performance connectivity for AI workloads?

The rapid, synchronized movement of high volumes of data requires congestion-free AI networking fabrics, low-latency AI networking architectures and autonomous operations.

Configuring high-throughput networking for large-scale AI data pipelines rests on an array of AI networking advances, including:

Compute fabric
AI-era computing demands that network fabrics be lossless and congestion-free lest compute slows and GPUs sit idle — a wasteful and expensive state — because all of them must finish their current task before the next one can begin. Networks capable of up to 1.8 Tbps fabrics are emerging to meet the intense bandwidth needs of model training. Engineers weigh requirements for ultra-low latency and bandwidth, open standards and interoperability, and cost and infrastructure familiarity.
Data processing
Smart network interface cards (SmartNICs) and data processing units (DPUs) have emerged as a way to offload complex tasks from the CPU so it can devote more compute to application processing. These programmable network adapters have their own processing units, enabling them to handle storage, security, and data management, among other workloads.
Optical interfaces
As AI network bandwidth needs intensify, traditional transceivers can slow the speed of data flowing to and from processing hardware. Integrating optical components closer to GPUs enables faster data transmission, thus reducing latency. Innovations such as co-packaged optics, linear-pluggable optics, and silicon photonics not only increase performance, they also reduce power consumption, which is a central concern of AI data center operators.
Switches
To support AI workloads, networking switches must be able to usher along immense amounts of data between connection points at lightning speed, often in intense bursts that can overwhelm conventional network switches. AI networking topologies employing smart switches engineered to move traffic continuously between AI accelerator chips can handle massive information flows without logjams or data loss thanks to innovations such as advanced congestion control and adaptive routing. They also incorporate intelligent power management to reduce energy consumption and improve GPU monetization.
Liquid cooling
GPUs aren’t the only power-hungry, heat producing hardware in the AI data center. With its ability to dissipate heat more energy-efficiently than air cooling systems, liquid cooling cold plates manage the thermal excesses of AI networking fabric switch ASICs, too.
Open-source software
Vendor lock-in is a data center operator concern. Software for Open Networking in the Cloud (SONiC) is an open-source network operating system that enables companies to use the same networking software stack across a range of devices from different switch vendors for greater flexibility, scalability, and modularity.

How do you manufacture sophisticated AI networking technologies at scale?

From a manufacturing standpoint, the takeaway is that data center operators must choose partners with the engineering expertise, production capabilities, and resilient supply chains to deliver sophisticated, high-quality AI networking components at scale. When unprecedented demand meets technological complexity and zero tolerance for performance issues, choosing wisely is essential.

Look for a partner with:

Blue line-art illustration of three server cabinets with various panels, buttons, and compartments representing data center equipment.

Proven ability to manufacture complex, advanced data center technologies at the high volumes that hyperscalers, cloud providers, and colocation facility operators need to deliver on their compute capacity and performance mandates.

Blue line-art illustration of a microchip containing a stylized brain to represent artificial intelligence or machine learning.

Deep knowledge of AI networking technologies paired with data center infrastructure expertise to help operators make holistic, well-informed decisions across multiple parameters, from bandwidth and latency considerations to power/cooling efficiency and deployment timelines.

Blue line-art illustration of a construction worker wearing a hard hat and overalls.

Technicians at key manufacturing locations around the world with disciplined execution to meet SLAs and quality standards, who can handle complex AI networking assembly, intricate fiber routing, optical component integration, and more.

Blue line-art illustration of a pencil drawing over a set of dashed measurement lines with arrows indicating vertical and horizontal dimensions.

Design and engineering services with a focus on innovation, product excellence, and manufacturing readiness to minimize risk as production ramps up with increasing demand, and to understand how performance may vary over time.

Blue line-art illustration of a medical monitor displaying a heartbeat waveform with various buttons and controls on the right side.

Testing and validation services to help ensure product performance, reliability, and durability throughout the product lifecycle.

Person standing in a large data center holding an open laptop and working while surrounded by rows of server racks optimized for networking for AI data centers

Advanced AI networking capabilities are a competitive differentiator for data center operators as AI workloads proliferate and substandard networks become a bottleneck. Unprecedented data traffic is driving innovation across AI network topologies to improve performance, security, and scalability.

To learn more about Flex’s role in deploying networking infrastructure for AI data centers, visit flex.com/industries/communications

FAQ: Networking for AI

What is networking for AI?

Networking for AI refers to the network architectures, systems, and technologies that connect GPUs and other IT hardware. They are designed to address the bandwidth, latency, throughput, and reliability demands of AI workloads.

How does networking for AI differ from traditional networking?

AI workloads can push data flows to terabits per second (Tbps), far exceeding legacy 25 Gbps/100 Gbps networks. To keep GPUs fully utilized, AI clusters require high bandwidth, lower latency, and lossless fabrics.

What does “scaling up, out, and across” mean in AI networking?

Scaling up: Increase per-GPU bandwidth inside the rack with strict signal integrity

Scaling out: Connect racks within a facility using congestion-aware fabrics

Scaling across: Unite GPUs across data centers using low-latency, encrypted fiber optic fabrics

What is a compute fabric in AI data centers?

A compute fabric is a high-speed, lossless network that enables multiple GPUs to operate as one. Fabrics are commonly designed to support 400 Gbps, 800 Gbps, and emerging 1.6 Tbps to 1.8 Tbps data flows.

How do SmartNICs and DPUs help AI workloads?

SmartNICs and DPUs offload storage, security, and data management tasks, freeing CPU cores for more intensive computing.

Why are optical interfaces important for AI clusters?

Co-packaged optics, silicon photonics, and linear pluggable optics place optics closer to GPUs and ASICs for better performance at scale.

What makes a switch “AI-optimized”?

AI-ready switches sustain bursty east-west traffic with advanced congestion control, adaptive routing, deep buffers (where appropriate), and intelligent power management.

When is liquid cooling needed in AI networking?

Liquid cooling is used when switch and AI accelerator densities push heat loads beyond the capabilities of air-cooling systems. It improves energy efficiency and thermal stability.

What is SONiC and why do data centers use it?

SONiC is an open-source network operating system that works with many different switch vendors, reducing product lock-in and simplifying large-scale AI network operations.

How should networking fabrics handle synchronized, bursty GPU traffic?

Use lossless or near-lossless design with congestion signaling, adaptive routing, and path diversity to prevent head-of-line blocking and keep GPU pipelines fed.