Many companies start running AI workloads on existing infrastructure. The results are often disappointing: overheating racks, insufficient power, or latency that interferes with both training and inference. Colocation can be a good solution—but only when chosen with AI’s unique characteristics in mind.
Here are four practical points worth checking before moving or starting AI workloads in a colocation facility.
1. Higher Power Density
AI servers, especially those using GPUs, draw far more power per rack than traditional servers. Some modern configurations reach 30–50 kW per rack or higher.
Ask the colocation provider what power density is available per rack and whether they can increase capacity as your needs grow. Look beyond total facility power and focus on availability at the specific rack level you will use.
2. Cooling That Handles GPU Heat
GPUs generate highly concentrated heat. Standard air cooling is often not efficient enough. Many facilities now offer liquid cooling or hybrid cooling for high-density racks.
Make sure you understand the available cooling options and how the process of migrating to more advanced cooling would work if needed later. This can save significant operational costs over time.
3. Connectivity and Latency
For real-time inference or distributed training, latency and bandwidth matter a great deal. Colocation facilities connected to multiple carriers and internet exchanges usually provide better flexibility.
If you use a hybrid cloud setup, also check direct connectivity to the cloud providers you rely on. Some data centers offer private interconnects that meaningfully reduce latency.
4. Scaling Flexibility and Remote Hands
AI workloads often grow quickly. Ensure the provider has clear procedures for adding power, racks, or even replacing hardware within a reasonable timeframe.
Responsive remote hands services are also important, especially when hardware failures on GPU servers require fast physical intervention.
Closing
Running AI in colocation is not just about moving servers to a more stable location. It is about making sure the infrastructure is ready for different power, cooling, and connectivity characteristics. Considering the four points above from the start reduces the risk of bottlenecks that only appear after the workload is already live.
