Running AI at Scale: Getting Beyond the GPU

AI isn’t magic, it’s infrastructure. And once you try to scale it, the real work begins. Latency becomes critical. Power and cooling become gating factors. Compliance? Suddenly not optional. Scaling AI isn’t about hype. It’s about smart architectural choices.

Here are five things that matter when you’re building infrastructure for AI workloads that need to work reliably, efficiently, and at scale:

1. Put Inference Close or Pay the Latency Tax

Real-time use cases—object detection, fraud scoring, voice response—live or die on latency. Sub-10 ms isn’t a stretch goal, it’s table stakes. That means moving inference workloads closer to users, sensors, and systems, not backhauling everything to the cloud.

Infra checklist:

• Deploy inference nodes in metro-edge colocation near data sources
• Monitor end-to-end latency from app to user, not just server metrics
• Use dedicated cloud on-ramps or private peerings
• Filter, normalize, and compress data at the edge

2. Hybrid Cloud Without the Glue and Gaps

Training often happens in the cloud, and access to scalable GPU clusters makes that straightforward. But when you shift to fine-tuning or serving, especially with data that’s local or latency-sensitive, you need infrastructure that doesn’t slow you down or introduce friction.

Practical setup:

• Establish BGP sessions and VRFs between environments to keep routing clean
• Use infrastructure-as-code to spin up consistent deployments (Terraform, Ansible, etc.)
• Align container runtime environments across cloud and edge to simplify DevOps handoff
• Set up distributed object storage to replicate models and artifacts across zones

3. Racks That Can Take the Heat (Literally)

Running 8-GPU boxes or clustered inference nodes? You’ll hit 30–50 kW per rack before you know it. Legacy enterprise facilities weren’t built for that. You need power and cooling that match the density of the workloads, not just survive them.

Build for:

• 30–50 kW racks, even up to 100 kW, with liquid-to-chip cooling or rear-door heat exchangers
• Hot/cold aisle containment with dynamic airflow management
• PDUs with outlet-level power telemetry and alerting
• Rack placement planning based on real thermal load data, not estimates

4. Power and Cooling as First-Class Citizens

Energy efficiency isn’t just good for ESG reports; it’s critical to keeping costs under control and infrastructure online.

Optimize:

• PUE under 1.3 using zone-aware airflow and raised inlet temps (ASHRAE A3/A4)
• Monitor power draw per node and per workload with smart PDUs
• Integrate metrics into Prometheus/Grafana for real-time visibility
• Avoid oversubscription: design to peak load, not average

5. Secure and Local by Default

You can’t fake compliance. Especially when handling regulated data or running workloads under strict geographic constraints.

Secure the stack:

• Use scheduler-level placement constraints to enforce data locality
• Encrypt data in transit, east-west and north-south
• Microsegment internal traffic using eBPF-based enforcement (Cilium, Calico)
• Lock down management interfaces (IPMI, PXE, serial) and audit access logs

AI infrastructure isn’t about throwing more hardware at the problem. It’s about knowing where your bottlenecks are—and designing around them.

Build for latency, density, and control. Everything else follows.

For a dive on why inference doesn’t belong in the cloud alone, check out our post: AI Inference Doesn’t Belong in the Cloud (Alone).

How Kolo can help

Kolo is a Northern European colocation platform with facilities in the Netherlands, Denmark, and Sweden. We off

• Metro colocation in NL, DK, and SE—carrier-dense, ISO-certified, and interconnected
• High-density racks designed for GPUs and accelerators
• Remote Hands in all locations
• Access to low-cost, renewable energy (especially in Sweden)
• Private connectivity to clouds, carriers, and ecosystem partners

We help infrastructure teams deploy AI where it needs to be.

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