Datacenter physical infrastructure (1/4): the power chain

Contents

This blog has devoted more than a hundred articles to the software layer of inference: serving engines, KV-cache, observability, FinOps. But all of that rests on something far more prosaic and far less forgiving when it fails: the current coming in through the wall. With this series we open a new vertical, the physical infrastructure of the datacenter, the cross-cutting topics an AI architect tends to take for granted until the day a power cut takes down an entire cluster. We start at the very beginning: the power chain.

The approach is deliberately mixed. The concepts (electrical chain, redundancy, Tier levels, power factor) are universal and apply to any datacenter. But in each section we will point out why the AI era, with its racks of 120 kW and more, stresses that infrastructure to extremes the classical design never anticipated.

Why power is the foundation of everything

In a datacenter, power is not an ancillary service: it is the primary constraint. You can survive a slow disk or a congested network, but not the loss of supply to critical equipment. And unlike software, electrical infrastructure is not hot-patched or scaled with a kubectl scale: it is designed, built, and lived with for fifteen or twenty years. Getting the sizing or the redundancy wrong is not a bug, it is a construction project.

In 2025-2026 this has gone from being a facilities concern to being the bottleneck of the entire sector. Power availability has become the factor that limits where and when an AI datacenter can be built: grid constraints stretch connection lead times to between 24 and 72 months, made worse by the global shortage of transformers, switchgear and even gas turbines. The challenge used to be finding GPUs; now, increasingly, it is finding megawatts.

Power travels a long road from the utility feed to the chips. Each link transforms, protects or distributes, and each one is a point where reliability is won or lost. The canonical sequence is this:

Grid (medium voltage) → MV switchgear → MV/LV transformer → low-voltage boards → UPS → floor power distribution units (PDU) → rack PDU or busway → server power supplies.

The utility feed arrives at medium voltage (MV) for facilities whose IT load exceeds a megawatt, which is already most of the relevant ones. Typical input voltages range from 11 to 33 kV. The MV switchgear is the entry and protection point; vacuum circuit breakers are today the indoor standard. From there, the transformers (usually 2.5 or 3 MVA) step the MV down to low voltage (LV), typically 400-480 V, which is what the hall handles.

Downstream of the transformer, the low-voltage boards distribute power towards the heart of the protection system: the UPS (uninterruptible power supply). The UPS performs two functions worth keeping separate: it conditions the power (filtering disturbances, voltage sags, harmonics) and, above all, it bridges the seconds the generator takes to start and pick up load when the grid fails. The dominant topology in serious installations is double-conversion online, which isolates the load completely from the incoming supply. We will return to the UPS in the fourth article of the series, because its relationship with the generator is the heart of resilience.

After the UPS, the floor PDUs (Power Distribution Units) feed the rows, and the rack PDUs (or an overhead busway) deliver power to each cabinet. Modern PDUs are not dumb power strips: metered units measure consumption per outlet and intelligent ones integrate with DCIM, add environmental sensors and allow outlets to be switched remotely. Finally, the server power supplies convert that LV into the DC voltages the components consume.

The A/B dual path

There is a design principle that runs through the whole chain in high-availability installations: the dual A and B path. Instead of a single route from the transformer to the rack, two electrically independent paths are built, with separate circuits, boards, UPS, PDUs and breakers, and no common points of failure. Every critical server carries two power supplies (dual-corded) and connects simultaneously to path A and path B. If one path goes down entirely, whether through a fault or planned maintenance, the server keeps running on the other without flinching. This architecture is the physical basis of 2N redundancy, and it is what allows half the installation to be maintained while the other half keeps serving.

Redundancy: the language of the “N"s

The whole conversation about electrical reliability is encoded in a compact notation worth mastering, because it defines the cost and the availability of the facility:

  • N is the strict minimum capacity to support the load, with no reserve at all. A failure of any component brings the system down.
  • N+1 adds one backup component to the set. It is the most efficient option in cost and energy, and the most common in cooling and, often, in generation.
  • N+2 adds two backups, to tolerate a second failure or a failure during the maintenance of another component.
  • 2N duplicates the entire system: two complete, independent sets (the A and B paths). It tolerates the total failure of either path with no impact, and allows one half to be maintained while the other operates. It is the basis of fault tolerance.
  • 2N+1 is the 2N duplication plus an extra component, to survive a failure and a maintenance activity at the same time.
  • Distributed redundancy (block redundant, sometimes “3N/2”): instead of backups dedicated to each block, it shares the reserve among several blocks, seeking a middle ground between the cost of N+1 and the robustness of 2N.

Two operational concepts sum up the intent of all this. A system is concurrently maintainable when any component can be taken out of service in a planned way without affecting the IT load; and it is fault tolerant when it also survives any single, unforeseen failure. The difference between the two is, essentially, the difference between being able to do maintenance without an outage and being able to absorb an unexpected event during that maintenance as well.

The economic trade-off is the axis of the whole decision. Going from N to N+1 adds a single component and is relatively cheap; going to 2N literally duplicates half the installation (two UPS, two paths, often two electrical rooms) and drives up capex, floor space and the no-load consumption of the redundancy alike. Distributed or block redundancy exists precisely to capture much of the robustness of 2N at a cost closer to N+1, sharing the reserve among several blocks instead of dedicating it. There is no universal answer: the right architecture is the one that matches the cost of unavailability for your business against the cost of avoiding it. For a critical workload under SLA, the usual order of magnitude is a concurrently maintainable distribution with a dual A/B path; for workloads that tolerate maintenance windows, N+1 can be perfectly reasonable and much cheaper.

The Uptime Institute Tier levels

The most widespread way of classifying this robustness is the Uptime Institute Tier system, with four levels in Roman numerals:

  • Tier I (basic capacity): single components, a single distribution path, no redundancy. Any maintenance or failure means an outage.
  • Tier II (redundant capacity): redundant components (N+1 in capacity) but a single, non-redundant distribution path.
  • Tier III (concurrently maintainable): multiple independent distribution paths; any component can be taken out for maintenance with no impact. It usually combines 2N in power with N+1 in cooling.
  • Tier IV (fault tolerant): 2N in every system; it survives any single infrastructure failure, as well as being concurrently maintainable.

Here an important warning is needed that many guides leave out. It is very common to see tables associating each Tier with an availability percentage (99.671 % for Tier I, 99.741 % for Tier II, 99.982 % for Tier III and 99.995 % for Tier IV) translated into minutes of downtime per year. Those percentages are not guarantees or official Uptime Institute definitions. In fact, Uptime removed the references to “expected annual downtime” from its standard in 2009, and maintains that its definitions are based on topology and behaviour (redundant components, concurrent maintainability, fault tolerance), not on a magic number of nines. A badly operated Tier IV can go down, and a well-operated Tier III can have a spotless service record. Quote those percentages, if at all, as popular reference figures, never as a promise.

Two more nuances for an architect. First, design is not the same as built: Uptime distinguishes the Tier Certification of Design Documents, which validates only the drawings and expires after two years, from the Constructed Facility certification, which verifies the real building with live demonstrations. A “designed for Tier III” without a constructed-facility certification is an intention, not a fact. Second, the European alternative: the EN 50600 standard, now folded into ISO/IEC 22237, classifies with Availability Classes 1 to 4 analogous to the Tiers, but with a more holistic approach covering power, cooling, telecommunications, security and efficiency, and it can be certified by independent bodies. In Europe it pays to speak both in Tiers and in EN 50600 classes.

The metrics you really have to understand

Beyond the nomenclature, there are three pairs of concepts an architect must handle in order not to get the sizing wrong.

kW versus kVA and the power factor

This is the most common sizing error. kW (active or real power) is the power that does useful work: what the servers actually consume. kVA (apparent power) is the power the electrical infrastructure (the UPS, the cables, the generator) has to be capable of delivering. The relationship between the two is the power factor (PF):

$$\text{PF} = \frac{P_{\text{kW}}}{S_{\text{kVA}}}$$

From which you get what really matters when sizing: the apparent power the equipment must supply,

$$S_{\text{kVA}} = \frac{P_{\text{kW}}}{\text{PF}}$$

If you size a UPS or a generator by the kW of the load alone, ignoring the power factor, you underestimate the capacity needed. Modern servers with active power factor correction work close to unity (PF from 0.9 to 0.99), but generators are usually specified at a PF of 0.8: a 1000 kW set is catalogued as 1200 kVA. And a 100 kVA UPS at PF 0.9 delivers only 90 real kW. A load of 150 kW at PF 0.8 demands 187.5 kVA of equipment, not 150. Confusing these two quantities has left more than one hall with no headroom the day it filled up.

PUE: the efficiency of the whole facility

PUE (Power Usage Effectiveness) measures how much total energy the facility consumes for every unit that actually reaches the IT equipment:

$$\text{PUE} = \frac{E_{\text{total}}}{E_{\text{IT}}}$$

A PUE of 1.0 would be the theoretical ideal (all the energy goes to IT, nothing is lost in cooling, conversion or lighting). The sector’s declared average is around 1.5-1.56, the big hyperscalers come down to 1.09-1.20 (Google reports around 1.09) and regulation is tightening: the German energy efficiency act requires a PUE of 1.2 or lower for new datacenters from 2026. PUE is, at bottom, a measure of how much energy “tax” the facility pays above what it computes.

Availability and the nines

Finally, availability is expressed in “nines”, and it pays to have internalised its translation into annual downtime: 99.9 % is almost 9 hours a year; 99.99 %, about 53 minutes; 99.999 % (the famous five nines), about 5 minutes. The economic key is that the cost of each additional nine is not linear: going from four to five nines does not cost a little more, it costs a multiple of the investment in redundancy, automation and, often, multiple sites. Chasing nines the business does not need is one of the most expensive forms of over-engineering.

A sizing example

Let us see how these quantities fit together in a concrete case, because the devil is in the detail. Suppose a row of eight GB200-class inference racks at 130 kW each: 1040 kW of real IT load. To size the UPS, which works on apparent power, we apply the load’s power factor (say 0.95):

$$S_{\text{kVA}} = \frac{1040}{0{.}95} \approx 1095 \text{ kVA}$$

that is, we need a UPS of around 1100 kVA for this row alone, and that without counting growth headroom or redundancy. To size the generator, on the other hand, the IT load is not enough: you have to add everything hanging off the emergency side, above all the cooling, which at these densities is liquid and consumes its share. If we apply a PUE of 1.3, the total facility load for this row comes to around 1350 kW, and since sets are rated at PF 0.8, the generator must deliver on the order of 1690 kVA. You would choose a 2 MVA set, and an N+1 architecture would need two of them. The jump is revealing: a row that “consumes 1 MW” demands, via power factor and cooling, more than 4 MVA of installed generation if you want redundancy. Anyone sizing in their head, multiplying racks by chip watts, falls short at every link.

The AI earthquake: rack density

This is where the AI era breaks the assumptions. For decades, rack density sat between 5 and 15 kW, and the sector average has barely risen: the Uptime Institute’s 2025 survey puts it at 7.5 kW per rack, with the bulk still between 5 and 9 kW. The whole classical electrical chain, and air cooling with it, was designed around those figures.

A current AI rack pulverises them. The NVIDIA GB200 NVL72 is specified at 120 kW per rack, and real deployments have measured 130-132 kW at full load: eight to ten times the density of a traditional rack, concentrated in the same cabinet. At that density air stops being viable (rear-door units of 30-40 kW fall short) and direct-to-chip liquid cooling goes from option to requirement. And the electrical chain has to carry those 130 kW to a single rack: dedicated three-phase circuits, PDUs of 200 A or more, a lot more copper.

And this has only just begun. NVIDIA’s roadmap points to some 190-230 kW per rack with the Vera Rubin generation, and to a staggering 600 kW per rack with Rubin Ultra “Kyber” in 2027, which NVIDIA proposes to feed directly at 800 V DC to reduce conversion stages and copper. The sector is already talking openly about 1 MW-class racks. These figures should be taken for what they are, vendor roadmap and industry debate rather than deployed installations, but the direction is unmistakable: density is rising faster than traditional electrical infrastructure knows how to absorb.

The aggregate impact is macroeconomic. Datacenters consumed around 415 TWh in 2024 (about 1.5 % of the world’s electricity), and the International Energy Agency projects that the global figure could approach 945 TWh in 2030, pushed by AI. In 2025 datacenter electricity demand grew by 17 %, and that of AI-specific datacenters by 50 %. That is why grid connection, and not silicon, has become the real limiting factor for many projects.

High-density distribution and the jump to 800 V DC

Taking 130 kW, and tomorrow 600 kW, to a single rack forces a rethink of distribution. By Joule’s law, carrying more power at the same voltage means more current, and more current means more copper, more losses and more heat in the distribution itself. The industry is responding along two lines. One is raising the internal distribution voltage: NVIDIA has proposed for its next-generation racks a direct supply at 800 V DC, which reduces AC-DC conversion stages, cuts the copper needed and improves efficiency against the classical scheme of 480 V AC converted to DC in several steps inside the rack. It is an emerging transition and still under debate, but it marks the direction. The other line is the high-current overhead busway instead of traditional PDUs, which makes it easier to reconfigure and scale power per row without rewiring. For an architect, the lesson is that distribution topology (voltage, busway, number of phases) stops being an installation detail and becomes a design decision with a direct impact on cost and on the maximum density achievable.

The grid as the limit: siting and on-site generation

There is a link upstream of the whole chain we have described, and in 2025-2026 it has become the most critical of all: the connection to the utility grid. Getting the grid to deliver tens or hundreds of megawatts at a specific point has become the sector’s real bottleneck. Connection lead times have stretched to between two and six years in many geographies, weighed down by substation saturation and by the global shortage of transformers and medium-voltage switchgear, whose delivery times are now counted in years, not months.

This has pushed a trend worth knowing about even though it falls outside the classical chain: on-site generation (behind-the-meter). More and more AI projects, unable to wait for the grid, install their own generation (gas turbines, gas engines, and in some cases fuel cells) to start up before having a firm connection, or so as not to depend on one. It is an inversion of the traditional paradigm, in which the generator was only an emergency backup: now, sometimes, it is the primary source while the grid is awaited. This dynamic has cost, emissions and regulatory implications that an AI architect would do well to keep on the radar, because they determine where you can build and how fast. The siting of an AI datacenter is, increasingly, a decision about power availability before anything else.

The facility is only as reliable as its operation

It is worth closing with a warning the Tier system already hinted at: topology is a necessary but not a sufficient condition. A facility with impeccable 2N redundancy can go down all the same through a botched switching operation, maintenance without a procedure, a mislabelled breaker or a transfer test that was never rehearsed. A substantial share of real outages is born not of an equipment failure but of human operation on that equipment. That is why the Uptime Institute insists that a badly operated Tier IV guarantees nothing, and why constructed-facility certification includes live demonstrations: seeing the system transfer load for real, not reading it off a drawing.

The practical consequence is that electrical reliability is managed with the same disciplines as any critical system: observability and rehearsal. Observability is provided by DCIM (Data Center Infrastructure Management), which instruments the whole chain (consumption per outlet at the PDUs, temperature, status of the UPS and the generators, fuel levels, switch positions) and allows the system to be seen in real time and problems to be anticipated. Rehearsal is provided by periodic testing: starting the generators under real load (with load banks if necessary), running full transfers to the emergency source, and verifying that the UPS holds the bridge. A continuity plan that has not been rehearsed at realistic scale is a hypothesis, not a guarantee; and the day of the power cut is not the moment to discover that the generator will not pick up load or that the UPS cannot hold the necessary seconds. The physical layer, like the logical one, rewards whoever measures and rehearses, and punishes whoever trusts the paperwork.

For an inference factory

What does someone building or running an inference factory take away from all this? Three ideas. First, that the electrical design must start from the real density of AI racks, not from historical averages: planning an inference hall at 10 kW per rack is planning for a facility that does not exist. Second, that liquid cooling and high-density distribution stop being exotic and become the base case, with everything that implies for sizing copper, PDUs and boards. And third, that redundancy has a cost that has to be matched to the business: an inference factory under SLA probably justifies a concurrently maintainable architecture (Tier III order, dual A/B path), but chasing full fault tolerance at every link can be a luxury the use case does not pay for. The availability of compute, as we saw in the storage series, depends as much on the physical layer as on the logical one.

This article has drawn the complete map of the chain. The next three go into the links where resilience against a power cut is decided: the generators that sustain the facility when the utility goes away, the switching (ATS and STS) that transfers the load between sources, and the UPS that bridges those critical seconds. Because a perfectly sized chain is worth nothing if, the day the grid blinks, there is nobody to take over in time.

See also

Sources