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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsPUE remains a useful measure of data-center facility overhead, but it does not show how much electricity is lost inside a server as power is converted and regulated before reaching an AI processor. In an October 2, 2026, Electronic Design article, Hans Hasselby-Andersen, CEO of Lotus Microsystems, argues that operators should pair PUE with measurements of server-internal power delivery. That is a proposal for a fuller view of efficiency, not an adopted industry standard.
What PUE measures—and where it stops
Power usage effectiveness (PUE) is the ratio of a data center’s total facility energy to the energy delivered to its IT equipment. It captures overhead needed to support IT, including cooling, lighting, and facility power distribution. A lower PUE indicates less facility energy overhead relative to IT energy.
The boundary matters: facility PUE ends at the IT equipment boundary. It does not separately account for losses inside a server’s power-delivery chain. An earlier Electronic Design explainer describes the same facility-to-IT ratio; it is background rather than a current standards source.
How power reaches an AI processor
Hasselby-Andersen’s article gives a simplified example of a server power path. Depending on the design, incoming AC power is converted by a power supply to a high-voltage DC bus; DC-DC stages then step the voltage down, and a point-of-load (POL) converter regulates it close to the processor. Each conversion and regulation stage can lose some energy as heat. Those internal losses are not itemized by facility PUE.
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This distinction does not mean PUE is inaccurate. It means PUE answers a facility-level question, while stage-efficiency measurements would help show what happens within the equipment boundary.
Why AI power levels sharpen the question
The Electronic Design article reports that typical data-center racks drew roughly 5 to 8 kW about five years before its October 2, 2026 publication, compared with 15 to 50 kW for current AI-facility designs; it also cites more than 100 kW for GPU-dense configurations. These are figures reported by the article, not independently verified industry averages here. Rack designs vary by generation, workload, and deployment.
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At higher power, a given percentage of conversion loss represents more absolute energy. The article also argues that accelerator load transients place additional demands on power electronics. It provides no independent test data quantifying the magnitude or distribution of those losses, so these points should be read as the author’s engineering argument, not measured results.
Keep PUE, add stage-level measurements
“PUE alone can no longer stand in for the full picture of AI data center efficiency,” Hasselby-Andersen writes. His point is not to discard PUE, but to supplement it with efficiency reporting at successive power-conversion stages, particularly near the point of load.
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| Measure | Question it answers | What it does not show by itself |
|---|---|---|
| PUE | How much total facility energy is used relative to energy delivered to IT equipment? | How much energy is lost in conversions and regulation inside a server. |
| Stage-level power-delivery efficiency | How does power change as it is converted and regulated within the server? | Facility overhead outside the measured server stages. |
The article proposes complementary measurements; it does not establish a new industry-wide metric or a standard test protocol. PUE and server-internal efficiency are not interchangeable scores, and neither alone describes every aspect of data-center impact or useful compute output.
Questions to ask when comparing vendors
When a vendor reports power-delivery efficiency, ask for enough detail to understand what was measured. The article recommends stage-by-stage questions but does not prescribe a complete protocol; the following comparison points help make unlike claims easier to identify.
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- Where is the measurement boundary? Ask which input and output points define each conversion stage, and whether the result covers the full server or only a component.
- Under what operating conditions? Request the load profile, transient behavior, voltage and current conditions, and operating point used for the measurement.
- How is efficiency reported? Clarify the measurement method and whether the figure is instantaneous, averaged over a workload, or rated at a specified operating point.
- What happens near the processor? Ask how point-of-load regulation performs under the relevant load and transient conditions.
Without defined boundaries and comparable operating conditions, two efficiency figures may not describe comparable systems. Avoid treating a single vendor number as a ranking unless the measurements use a common basis.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to read the article’s product example
Hasselby-Andersen identifies Lotus Microsystems’ vStrata as a vertical power module intended for ultra-high-current AI accelerators. Because he is the company’s CEO, this is a vendor example, not independent evidence of performance. The article does not establish savings or compatibility for a particular system; those claims would require applicable product specifications and comparable measurements.
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For engineering validation rather than general purchasing, a separate Electronic Design article describes Tektronix’s EA ELR 21000 Dynamic Test System as an electronic load for emulating AI-driven power conditions when testing supplies. That product-focused coverage reports vendor specifications; the equipment is specialized test gear, not a consumer recommendation.
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