Determine networking requirements for AI workloads: Worked Example — AI Infrastructure (NVIDIA-Certified Associate: AI Infrastructure and Operations)

Determining Networking Requirements for AI Workloads: Worked Example In AI infrastructure, networking plays a critical role in ensuring efficient...

Determining Networking Requirements for AI Workloads: Worked Example

In AI infrastructure, networking plays a critical role in ensuring efficient data transfer, low latency, and high throughput to support demanding AI workloads. This worked example demonstrates how to determine the networking requirements for an AI training cluster, applying foundational principles relevant to the NVIDIA-Certified Associate: AI Infrastructure and Operations certification.

Scenario

A company plans to deploy an on-premises AI training cluster consisting of 16 NVIDIA GPUs distributed across 4 servers. Each server hosts 4 GPUs interconnected via NVLink. The AI models require frequent synchronization of large parameter sets during distributed training. The goal is to design a network infrastructure that supports efficient communication between servers to minimize training time.

Step 1: Identify Data Transfer Patterns and Bandwidth Needs

Distributed AI training involves exchanging gradients and model parameters between GPUs across servers. These communications are bandwidth-intensive and latency-sensitive.

Assume each model synchronization exchanges 100 GB of data per iteration.
Target iteration time is 1 second to maintain training efficiency.

Required network bandwidth per iteration:

100 GB per second translates to approximately 800 Gbps (since 1 byte = 8 bits).

This indicates the network must support near 800 Gbps throughput to avoid bottlenecks.

Step 2: Select Appropriate Network Technology

Common high-speed datacenter networking options include 100 GbE, 200 GbE, and 400 GbE Ethernet, as well as InfiniBand.

100 GbE per link is insufficient for the required bandwidth.
Using multiple 100 GbE links aggregated could meet bandwidth but adds complexity.
200 GbE or 400 GbE Ethernet or HDR InfiniBand are preferred for high throughput and low latency.

Decision: Deploy 400 GbE switches and network interface cards (NICs) to provide ample bandwidth and future scalability.

Step 3: Determine Network Topology and Switch Requirements

To minimize latency and maximize throughput:

Use a leaf-spine topology with 400 GbE spine switches connecting all leaf switches.
Each server connects to a leaf switch via dual 400 GbE NICs for redundancy and bandwidth aggregation.

This topology ensures non-blocking communication paths between servers.

Step 4: Consider Network Protocols and Features

AI workloads benefit from protocols that reduce latency and improve data transfer efficiency:

Use RDMA over Converged Ethernet (RoCE) to enable direct memory access between GPUs across servers, minimizing CPU overhead.
Implement Quality of Service (QoS) to prioritize AI traffic.
Enable congestion control mechanisms to avoid packet loss.

Step 5: Evaluate the Role of a Data Processing Unit (DPU)

A DPU can offload networking, security, and storage tasks from the CPU, improving overall performance.

Integrating DPUs in servers can accelerate network packet processing and enforce security policies.
This reduces CPU load, allowing more resources for AI computations.

Step 6: Verify Facility and Power Considerations

High-speed networking equipment requires sufficient power and cooling:

Ensure the datacenter supports the power draw of 400 GbE switches and NICs.
Confirm cooling systems can dissipate heat generated by networking hardware.

Summary

For the AI training cluster:

Network bandwidth requirement: ~800 Gbps per iteration.
Recommended network: 400 GbE leaf-spine topology with dual NICs per server.
Protocols: RoCE for low-latency communication.
Additional hardware: DPUs to offload network processing.
Facility: Adequate power and cooling for high-performance networking gear.

Worked Example Recap

Problem: Design networking for a 16-GPU AI training cluster requiring 100 GB data synchronization per second.

Solution Steps:

Calculate bandwidth need: 100 GB/s = 800 Gbps.
Select 400 GbE network technology to meet bandwidth and latency requirements.
Design leaf-spine topology with dual 400 GbE NICs per server.
Use RoCE protocol for efficient GPU-to-GPU communication.
Incorporate DPUs to optimize network processing.
Ensure datacenter power and cooling support the infrastructure.

This approach ensures the network infrastructure supports the AI workload efficiently, reducing training time and improving scalability.

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