1. AI/ML Bandwidth and Latency Considerations

It is very important to understand your AI/ML requirements before architect your Network because there are different AI techniques as follows:

• Clusters

• Decision trees

• Neural networks

So to support these requirements, CPU is not enough any more but you need specialezed hardware accelerators such as GPU and TPU. (will talk about these later)

You need to be aware that the two main stages for AI/ML application lifecycle has two main stages:

1. Model Training

2. Inference (Descision-making)

The AI/ML application consists of processes that executed across multiple hosts, which each host perform the local calculations and syncronize the result with the other hosts.

Q: You need to ask yourself, Why AI/ML application need distributed processing:

A: AI/ML needs distrbuted processing for two resons:

• To speed up model training

• To handle many simultaneous inferencing queries coming from users

AI/ML models process large volumes of data in parallel across multiple GPUs in the cluster. The more GPUs you build into your cluster and the more data you can process, the faster and more accurate your training will be.

Training jobs are highly distributed, which means that a high volume of data flows between these components:

• Main memory and GPU memory via Peripheral Component Interconnect Express (PCIe) bus.

• GPUs in the same host using NVIDIA's high-bandwidth NVLink communication link.

• GPUs across different hosts in the cluster using the dedicated Ethernet network.

So it is very important to consider high-bandwidth for inter-communication between servers and each others,and to enable high-performance fabric so that all the GPUs in the cluster can behave like one large GPU.

AI/ML Application Networking Requirements

Training is the most resource-intensive phase of the AI/ML pipeline, demanding a high-throughput infrastructure for both compute and data movement. It involves long-running, full-rate data flows between compute nodes and storage systems. Additionally, training workflows include phases such as processing, notification, and synchronization—creating highly predictable yet bursty traffic patterns.

To ensure efficient execution, the underlying fabric must support predictable Quality of Service (QoS) with robust congestion management mechanisms. Optimal load balancing across the entire network is essential to fully utilize the available bandwidth.

Even a minor bottleneck—such as a single slow data flow—can impact the entire training job, as synchronization requires all flows to complete. Ultimately, the performance of the cluster is limited by its slowest component when it comes to training job completion time.

Training and Inference Network Requirements:

Model Training Network Requirements:

• High-bandwidth

• Non-blocking lossless network

• Congestion Management

Inferencing Network Requirements:

• Low latency

• Low Jitter

The learning cycles of AI/ML models can take days or weeks to complete, especially with massive datasets. To speed up the cycle, a high-performance network and capable computing and storage resources are needed.

So basically, we are building networks to serve business needs, so it is very important to understand what is the business needs.

In the next article will discuss about AI/ML Network Scalability and Redundancy Consideration.

#AIInfrastructure #MLNetworking #DataCenterDesign #GPUNetworking #AIFabric #NetworkArchitecture #ShehabWagdy