Critical Network Testing for AI Data Centres: The Key to Optimal Performance

It’s no secret that AI is changing the way modern infrastructure operates. LLMs like ChatGPT, Claude, and Llama 3 are more powerful than ever — and they’re placing huge demands on the data centres behind the scenes.

What many IT teams don’t see coming is just how much stress these new AI workloads put on their networks.

Traditionally, AI clusters have relied on proprietary interconnects like InfiniBand. But now, the industry is rapidly shifting toward Ethernet — especially 400G and 800G — to connect GPU clusters at scale. That shift isn’t just technical. It’s transformational.

And it comes with risks if you’re not prepared. AI Traffic isn’t normal traffic.

AI workloads behave differently from your typical enterprise applications. They’re bursty, synchronised, and extremely sensitive to latency and jitter. On top of that, they often rely on advanced networking protocols like RoCEv2, PFC, and DCQCN — which can be complex to configure and even harder to validate in production.

The result? Many organisations are flying blind into AI deployments, with no clear visibility into how their infrastructure will perform when it matters most. Validate Before You Deploy.

At Matrium Technologies, we’ve been helping teams across ANZ prepare for this shift. Our work focuses on pre-deployment validation — making sure your network can handle the real-world demands of AI before you go live.

We help you:

• Emulate realistic AI traffic patterns, like RingAllReduce and AlltoAll
• Validate job completion time and throughput across 400G and 800G networks
• Benchmark performance and uncover bottlenecks— before they impact production

The ROI of Network Testing in AI Infrastructure

Modern AI infrastructure is expensive. GPU clusters built on platforms like NVIDIA H100 or A100 cost millions of dollars, yet many sit underutilised due to unoptimised networking.

Without proper validation, your AI jobs may be stalling from:

• Poor queue management

• Misconfigured RoCEv2 or flow control

• Subtle bottlenecks in east-west traffic

Even 5–10% improvement in job throughput can yield hundreds of thousands in recovered value across training workloads.

Network Testing isn’t just risk mitigation — it’s a direct lever for return on GPU investment.

The Bottom Line

If you're planning to deploy AI infrastructure — or expand into high-speed Ethernet — you can't afford to guess how your network will perform.

Let’s have a conversation. We can help you validate, optimise, and accelerate your AI network rollouts — the right way.

• Validate before you deploy
• Optimise early
• Ensure performance
• Avoid surprises

At Matrium Technologies, we help organisations move from trial-and-error to confidence at scale with advanced network test solutions tailored for the AI era.