Artificial intelligence has changed what a server actually is. A modern AI server packed with GPUs draws ten to thirty times more power — and produces ten to thirty times more heat — than the ordinary rack-mounted equipment data centers were built around. This single fact breaks most colocation offerings on the market.
If you are training or running large language models, the question is no longer just “where do I put my server.” It is “which facility can physically power and cool it.” This guide explains what makes a data center AI-ready, the three ways to get GPU compute, and how to choose where to host GPU servers — with a focus on the situation in Kazakhstan and Central Asia.
Why AI breaks traditional colocation
For two decades, colocation was designed around a stable assumption: a rack holds general-purpose servers drawing roughly 5–10 kW, cooled by air. AI workloads violate that assumption on every axis.
Power density. A single 8-GPU AI server (for example an NVIDIA HGX node with eight H100 or H200 accelerators) draws 6–10 kW on its own. Fill a rack with them and you reach 40 kW, 80 kW, even 120–130 kW per rack for the newest B200-class systems. That is 10–30× the density of a conventional rack. Most data centers cannot deliver that much power to a single rack position, no matter how much floor space you rent.
Heat. Every watt of power becomes a watt of heat. Air cooling — pushing cold air through the room — runs out of capacity somewhere around 20–30 kW per rack. Above that, physics forces a switch to liquid cooling: rear-door heat exchangers, direct-to-chip cold plates, or full immersion. A facility that only offers air-cooled racks cannot host dense GPU clusters at all.
Network. Distributed training splits one model across dozens or hundreds of GPUs that must exchange gradients constantly. This demands low-latency, high-bandwidth interconnects (InfiniBand or RDMA-capable Ethernet at 200–800 Gb/s) between nodes — not the ordinary uplink a web server needs.
Continuous, high-value load. A training run can occupy an expensive GPU cluster non-stop for weeks. Unlike a web server that idles at night, AI hardware runs at full power around the clock, which raises the stakes on both reliability and the cost of electricity.
What makes a data center “AI-ready”
Before you place GPU servers anywhere, verify the facility can actually support them:
High power density per rack. Can the facility deliver 30, 50 or 100+ kW to a single rack or cage? Ask for the maximum supported density, not just the price per rack unit.
Liquid cooling support. For dense clusters, confirm the facility supports rear-door heat exchangers or direct liquid cooling, with the plumbing and heat-rejection capacity to match. Ask what density they can cool, not just power.
Sufficient total power. A GPU cluster is measured in megawatts, not kilowatts. The facility needs real, contracted power capacity at the site — and headroom to grow as you add nodes.
Reliability for long jobs. A power blip that restarts a two-week training run is expensive. Fault-tolerant infrastructure (see Tier IV, below) protects long-running workloads from single failures.
Low-latency connectivity. For inference serving users, proximity and network quality decide response time. For training, internal fabric between nodes matters most.
Three ways to get GPU compute
There is no single “best” option — it depends on how continuously you use the hardware.
1. Rent GPU instances in the cloud. You rent virtual GPU capacity by the hour. Fastest to start, zero hardware commitment, ideal for experiments, spiky demand, and short fine-tuning jobs. The trade-off: at high, steady utilisation the per-hour price becomes the most expensive option, and you don’t control the underlying hardware or where data physically sits.
2. Rent dedicated GPU servers. A provider owns physical GPU servers and rents them to you monthly. More predictable cost than hourly cloud, no capital outlay, but you’re limited to the provider’s hardware choices and still don’t own the asset.
3. Colocate your own GPU servers. You buy the GPU hardware and place it in a data center that provides power, cooling, network and security. You pay for space, power and cooling — not per GPU-hour. At steady, high utilisation over the multi-year life of the hardware, this is the lowest cost per GPU-hour, and you keep full control over configuration, data and physical access.
| Model | Best for | Cost at high utilisation | Control over hardware/data |
|---|---|---|---|
| Cloud GPU instances | Experiments, spiky or short jobs | Highest | None |
| Dedicated GPU rental | Predictable medium-term workloads | Medium | Limited |
| GPU colocation | Continuous, long-term workloads | Lowest | Full |
The rule of thumb: the more continuously your GPUs run, and the longer you’ll keep the hardware, the more colocation wins. Companies often start in the cloud to validate a use case, then move steady-state workloads into colocation to cut cost and regain control.
Training vs inference: different infrastructure
The two phases of AI have different infrastructure profiles, and conflating them leads to bad decisions.
Training is a batch job: enormous, dense clusters running at full power for days or weeks, tightly interconnected, extremely sensitive to interruption. Training rewards the highest power density, liquid cooling and fault tolerance. A single failure that restarts a run wastes real money.
Inference is a service: it must respond to users in milliseconds, scales with traffic, and cares about latency and geographic proximity. Inference often runs on smaller footprints closer to users, and its reliability requirement is about serving continuity rather than protecting a long computation.
A serious AI operation frequently splits the two — dense colocated clusters for training, distributed capacity for inference — and a good data center can host both.
Why long training jobs favour Tier IV
The longer and more expensive a training run, the more a single infrastructure failure costs. A power or cooling failure that forces a restart can waste days of computation and hundreds of thousands of dollars of GPU time.
This is where the reliability tier matters. A Tier IV facility is fault tolerant: it runs multiple active, independent power and cooling paths simultaneously, so any single component can fail without any impact on the load. For a two-week, million-dollar training run, that is not a luxury — it is protection for the investment. (For the full breakdown of reliability classes, see our guide on what Tier IV means.)
GPU colocation in Kazakhstan
For companies operating in Kazakhstan and Central Asia, hosting AI infrastructure locally has become both practical and, in many cases, legally necessary.
Data residency. Kazakhstani law requires the personal data of citizens to be stored on servers physically located in Kazakhstan. If your AI systems process such data, hosting them in-country is not optional. (See our guide on data localisation in Kazakhstan.)
Power at scale. AI clusters need megawatts. The regional constraint is rarely floor space — it is contracted power. A facility with substantial, redundant power infrastructure is what makes dense GPU deployment possible.
A cold-climate advantage. Cooling is one of the largest operating costs of an AI cluster. Kazakhstan’s continental climate, with long cold seasons, lowers the energy needed to reject heat and improves overall efficiency compared with hot-climate locations.
Reliability and proximity. A Tier IV facility in Astana keeps long training jobs safe from single failures while placing inference close to regional users, with low-latency connectivity across Central Asia.
Akashi Data Center in Astana is purpose-built for the AI era: Uptime Institute-certified Tier IV, with redundant 100 MW power infrastructure, 2N+1 redundancy across power and cooling, and four independent fibre entry points — the first and only Tier IV facility in Central Asia. For organisations placing GPU servers for training or inference, that combination of power capacity, fault tolerance and in-country data residency is exactly what dense AI workloads require.
Frequently asked questions
What is GPU colocation?
GPU colocation is placing your own GPU servers — for AI training or inference — in a third-party data center that provides the power, cooling, network and physical security they require. You own and control the hardware; the facility provides the environment.
Why can’t a normal data center host GPU servers?
A single AI server (for example an 8×H100 node) draws 6–10 kW, and a full rack of them can exceed 40–130 kW. Traditional colocation racks are designed for 5–10 kW and use air cooling, which cannot remove that much heat. AI density requires reinforced power distribution and, above a certain point, liquid cooling.
Should I rent GPU cloud instances or colocate my own servers?
Rent from the cloud for short, variable or experimental workloads — you pay per hour and launch in minutes. Colocate your own hardware when GPUs run continuously for months: owning the servers and paying only for space, power and cooling is far cheaper per GPU-hour at steady, high utilisation, and you keep full control over the hardware and data.
Do long AI training jobs need Tier IV?
The higher the value of a training run, the more a single power or cooling failure costs — an interruption can waste days of computation and expensive GPU time. Tier IV fault tolerance means any single component can fail without stopping the load, which is why long, high-value training clusters favour the highest reliability tier.
Where should a Kazakhstani company host GPU servers for AI?
In a data center physically located in Kazakhstan when the personal data of Kazakhstani citizens is involved, since the law requires it to be stored in-country. Beyond compliance, a local Tier IV facility with high power density, a cold climate that lowers cooling cost, and low-latency regional connectivity is well suited to AI workloads.
Planning a GPU deployment for AI in Central Asia? Explore Akashi’s colocation infrastructure — Central Asia’s first and only Uptime Institute-certified Tier IV data center, built for high-density, AI-era workloads in Astana, Kazakhstan.