By Sean Rinas, Head of Network Operations
As artificial intelligence moves from pilot to production, the pressure on infrastructure has shifted. It’s not just about whether you can run a model; your environment also needs to move fast enough, and scale cleanly with stability under load – all without racking up cost, risk or complexity in the process.
AI performance lives or dies by the scalability, reach and operational certainty of the network supporting it. Not just at the edge, or inside a rack – but across every connection between clouds, partners, services and systems. If your interconnection strategy can’t keep up, everything above it slows down.
Interconnection is becoming the critical lever for scaling AI. Now let’s unpack the seven traits that define whether your network is ready for what AI is demanding now.
1. Performance that scales without bottlenecksAI workloads eat bandwidth. Whether you’re training models or running inference, network capacity can’t be something you scale “eventually”. It needs to flex in real time – without manual intervention or architectural workarounds. The best networks are built on non-blocking designs, support high-density power and cooling, and connect directly into AI-ready environments like our nation-wide fleet of NVIDIA-certified DGX-ready data centres.
If your infrastructure and networks can’t keep up with your compute, you’re wasting time and money. Full stop.
2. Predictable, low-latency connectionsNetwork speed still matters. But if your network isn’t predictable, nothing runs the way it should. AI workloads can’t afford jitter, delays or surprises; especially when inference is happening in real time or at the edge. You need low-latency performance you can count on, not just fast on a good day.
When round-trip times are consistent, models behave as expected and outcomes stay reliable. Put simply, it’s what makes a network AI-ready.
3. Clean throughput with no congestion
You don’t run expensive GPUs just to watch them sit idle. Congestion wastes compute, because it clogs up workflows, drags out processing time and kills cost-efficiency. Traffic needs to be distributed evenly across the network, with intelligent routing that prevents bottlenecks before they occur.Your AI workload isn’t happy pausing for a traffic jam, and nor should your infrastructure.
4. Resilience built in, not bolted on
No matter how much resilience you’ve built in, networks can still fail. Links can drop and hardware ages (it happens to the best of us). The difference is how your infrastructure responds. Self-healing design, 100% geo-diverse redundant paths and zero-downtime maintenance are baseline requirements for production-scale AI.
That’s why NEXTDC’s interconnectivity platform AXON is engineered to stay up, stay fast and keep your traffic flowing, even when things break. Because they will.
5. Instant control, minus the ticket queue
AI projects rarely stick to fixed timelines or predictable workloads. Waiting three days to spin up a cross-connect kills momentum. Modern networks need to offer real-time provisioning, scalable bandwidth and seamless automation – all without relying on tickets or manual intervention.
It’s about giving teams the control to move at the pace the work demands.
6. Security that holds up under pressure
AI environments often carry large volumes of sensitive data, from training inputs to proprietary models and real-time outputs. That lifts the bar for security. Encrypting traffic between endpoints is now the bare minimum; the real challenge is protecting everything in between – including how systems and services connect. Encrypted traffic at Layer 2 as is offered by MACSec protocols should now be table stakes.
For organisations operating in regulated industries, or across borders, this also means having confidence in where data resides and how it moves. Sovereign infrastructure, jurisdictional control and secure interconnection are the basic foundation that make production AI viable.
7. Ecosystem proximity that speeds everything up
AI doesn’t work in isolation. It thrives in dense digital ecosystems where compute, data, storage and services are all within reach. Being close to the right clouds, partners and platforms is both a convenience and a performance/strategic advantage.
That’s why physical proximity, direct access and local routing now matter more than ever. When everything you need is a single hop away, latency drops, complexity shrinks, and your infrastructure becomes a lot easier to scale.
These seven traits define what it takes to run AI at scale today. From performance and predictability to security and ecosystem reach, they form the foundation of a network that won’t hold you back.
At NEXTDC, our focus is on helping customers build the infrastructure that enables AI - without the friction, delays or compromise. Whether you're training large models or deploying real-time inference, it’s the strength of your network that determines what comes next.
To benchmark where you stand, download our Interconnection Excellence Checklist. Or reach out to our team if you’d rather talk it through. We’re always up for a straight conversation about what’s working, what’s not and what comes next.