What we learned after implementing large-scale AI clusters, and how you can use it tomorrow.
As AI and machine learning (ML) technologies grow, enterprises are facing increasing challenges in managing and scaling their AI data centers. Specifically, AI model training and inference workloads demand high network throughput and low latency. Without the right infrastructure, network congestion becomes a significant bottleneck, slowing down performance and affecting overall operational efficiency.
What most teams do today?
- Many organizations rely on traditional, non-optimized network designs.
- Using smaller networks or single-tenant solutions without considering the specific requirements of AI and ML models.
Why it fails?
- High Latency & Bandwidth Issues: AI workloads, especially training, require massive amounts of data to be transferred across multiple GPUs. Insufficient network bandwidth and high latency slow down the training process.
- Network Congestion: When network congestion occurs, data transfer is delayed, negatively impacting model performance.
- Inadequate Load Balancing: Many systems still use static load balancing techniques that don’t adapt to the dynamic nature of AI workloads, further exacerbating congestion and reducing performance.
Framework / Approach
Step 1: Define
The first step in optimizing your AI data center is to define the specific needs of your AI workloads, both training and inference. Training often requires offline, large-scale computational resources, while inference usually demands real-time data processing.
Step 2: Diagnose
Evaluate the network infrastructure for scalability. A network that works for typical IT workloads might not meet the performance standards required for AI training or inference, especially with the increasing size of AI models.
Step 3: Decide
Invest in non-blocking, low-latency networks that ensure consistent data flow without interruption. Consider using specialized hardware like UCS C885A M8 Rack Servers, which offer up to 8 GPUs, and ensure that the network is optimized for GPU-to-GPU communication.
Step 4: Deliver
Deploy Quality of Service (QoS) strategies that prioritize AI-related traffic over other less sensitive data. Use technologies like PFC (Priority Flow Control) and ECN (Explicit Congestion Notification) to manage congestion in real time, ensuring that critical AI data flows smoothly through the system.
Case Study / Example
A large enterprise required an upgrade to its existing AI infrastructure, specifically addressing the growing demands for training large models across multiple GPUs.
Actions taken
- Replaced legacy switches with non-blocking, low-latency solutions.
- Implemented ECN and PFC to minimize congestion.
- Deployed Dynamic Load Balancing (DLB) to improve network traffic distribution and avoid congestion.
Results
- Reduced training time by 30%, allowing the organization to run more experiments simultaneously.
- Improved inference time, with AI models processing in real time without lag.
What didn’t work
While the improvements helped with congestion, further network tuning is needed for certain AI-specific workloads that require even higher bandwidth.
Playbook / Checklist
- Assess current network design to identify bottlenecks.
- Implement ECN and PFC for real-time congestion management.
- Use Dynamic Load Balancing (DLB) to distribute traffic evenly across multiple paths.
How to start in 30 minutes
- Analyze your current network’s capacity and AI workload requirements.
- Implement ECN and PFC for congestion control.
- Re-assess network performance after implementation and adjust as necessary.
Conclusion & Next Step
Optimizing your AI data center involves more than just adding more hardware; it requires a strategic approach to network design, load balancing, and congestion control. Start by addressing congestion with ECN and PFC and consider moving to a non-blocking network architecture for future scalability. In the next steps, continue to monitor performance and adjust infrastructure to match the growing demands of AI models.
At TelenceSolutions
We continue to help professionals build scalable, intelligent networks through real-world, hands-on learning — from OSPF and IS-IS fundamentals to BGP, SD-WAN, and AI-driven automation.