As AI becomes more embedded in our daily lives, supporting infrastructure must evolve to meet the surging demands.
While GPUs and data center design often attract the attention, networking is an equally critical pillar of AI infrastructure. Without robust networking, the most powerful compute resources cannot work in tandem effectively.
This article explains why networking is fundamental to AI infrastructure and how it supports AI at scale.
AI’s networking demands are unique
AI workloads are inherently data-heavy and time-sensitive. A single AI model like OpenAI’s GPT-4 is trained across tens of thousands of interconnected GPUs, working together in a cluster. These components must exchange data continuously and at very high speeds. For example, training runs often require chips to communicate hundreds of times per second, synchronizing parameters and gradients during each iteration.
This intense communication load means that low-latency, high-bandwidth networks are essential. Any delay or packet loss in the system can lead to inefficient training and idle compute resources..
Model training requires ultra-fast connectivity
The training of large language models (LLMs), image generation models or autonomous driving systems involves splitting computational tasks across massive compute clusters. Technologies such as NVIDIA’s NVLink, InfiniBand and Ethernet at 400 Gbps or higher are designed specifically to handle these requirements.
For example, InfiniBand is often preferred in AI clusters due to its low-latency and high-throughput properties, with speeds reaching 800 Gbps in the latest versions. NVIDIA’s DGX SuperPOD, a popular AI supercomputing solution, uses InfiniBand to connect up to thousands of GPUs with minimal communication delays. This infrastructure is essential to enable techniques like data parallelism and model parallelism, where parts of the neural network or dataset are distributed across nodes.
Inference also depends on networking
While training is resource-intensive, inference—the process of running a trained model to produce results—also requires fast and reliable networking. In AI applications like chatbots, fraud detection and medical diagnostics, milliseconds matter. Real-time inference demands low-latency communication between edge devices, cloud instanceand data storage.
Companies such as Google (TPU v5e), Microsoft (Azure AI) and Amazon (AWS Inferentia chips) are investing heavily in optimizing the network paths between AI accelerators and storage to reduce inference latency. This ensures users get quick, accurate responses regardless of where the request originates.
Massive data transfer and synchronization
Modern AI models are trained on petabytes of data, often spanning images, audio, video and text. This data must move from storage to processing nodes and back again, sometimes across regions or even continents. Without robust networking infrastructure, data ingestion, preprocessing, training and checkpointing would grind to a halt.
To handle this, cloud providers build dedicated high-speed fiber optic networks, sometimes spanning the globe. For example, Google’s private network spans over 100 points of presence worldwide, ensuring that data moves securely and quickly. Similarly, Microsoft’s Azure global network covers over 180,000 miles of fiber, connecting its data centers with low-latency pathways.
Scalability and redundancy: No room for downtime
As AI workloads scale, so does the risk of network failures. Redundancy, load balancing and intelligent routing are essential to maintaining uptime and performance. This is where software-defined networking (SDN) comes in, allowing operators to dynamically reroute traffic and optimize bandwidth based on real-time demand.
Looking ahead
The AI revolution is pushing networking infrastructure to its limits, and companies are responding with next-generation technologies. Future networks will increasingly rely on optical interconnects, custom switching fabrics and AI-driven traffic management tools to meet the growing demands.
Networking is the glue that binds AI systems together, enabling scalable, resilient and real-time performance. As models grow larger and more complex, investments in networking will be just as important as those in chips and power. For any organization planning to adopt AI at scale, understanding and optimizing the network layer is not optional—it’s critical.
