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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteNot necessarily. Running AI on an edge server or a phone changes where the electricity is used; it does not by itself prove that total electricity consumption rises. The International Energy Agency (IEA) says edge inference may reduce data-centre electricity use, with only a limited increase in device electricity in the examples it assessed. The overall effect depends on the workload, equipment, network and hardware lifecycle—and is not yet established globally.
What does it mean for AI to “leave the data centre”?
AI computation can happen in a large cloud or hyperscale data centre, in a smaller server closer to users (an edge data centre), or on an end-user device such as a laptop or smartphone. An edge data centre is still a data centre; the computation has moved closer to the user, not necessarily onto the user’s device.
The distinction also depends on the task. Training large AI models and much current AI-related computing demand remain centred in large facilities in the IEA’s 2025 account. Inference—the process of using a trained model to generate a response or make a prediction—is the activity that may shift to edge servers or devices.
Does on-device or edge AI use more electricity overall?
There is no universal answer. The IEA’s 2025 report says edge inference may lower data-centre electricity use, while increasing electricity use by devices only to a limited extent in the device examples it examined. Those examples are not a general per-query comparison: results depend on the model, hardware, workload, utilization, batching and what is counted.
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A fair comparison would hold the task constant and count operational electricity for the computing equipment, plus relevant cooling and power overhead where those data are available. It would also account for differences in server utilization and batching: a shared, heavily used server and a lightly used local device do not necessarily have the same energy use per task. The IEA material does not establish a universal apples-to-apples figure for these options.
| Where inference runs | What changes | What the evidence establishes |
|---|---|---|
| Large cloud or hyperscale data centre | Compute is concentrated in a large facility; results are delivered over a network. | The IEA describes large facilities as central to current AI-related demand. A universal per-task electricity figure is not stated in the IEA’s 2025 discussion of edge inference. |
| Edge data centre | Compute moves closer to users but remains in a data centre. | The IEA says edge inference may reduce data-centre electricity use; it does not provide a global net-energy total for this shift. |
| Phone, laptop or other end-user device | Some computation happens on the user’s hardware, potentially reducing the need to send data to a remote model for that task. | The IEA reports limited increases in device electricity for the examples it assessed, not a result that applies to every device, model or usage pattern. |
Why can AI get more efficient while total electricity use grows?
Efficiency per task and total electricity consumption measure different things. In its April 2026 follow-up, the IEA says energy use per AI task has fallen by at least an order of magnitude annually in recent years. It also reports that total data-centre electricity use grew 17% in 2025, while electricity use by AI-focused data centres grew 50%. More use and more energy-intensive applications can outweigh lower energy per task.
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Those figures describe different boundaries: the per-task trend concerns AI tasks, while the growth rates concern electricity used by data centres. They do not show that moving inference onto devices caused the increase.
How large is data-centre electricity demand?
In its 2025 report, the IEA estimated that data centres used 415 TWh of electricity in 2024, around 1.5% of global electricity consumption. That is data-centre use, not AI alone. The same report’s base case projected about 945 TWh in 2030, just under 3% of global electricity consumption; this was a scenario projection, not a later measurement.
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The IEA’s April 2026 outlook gives a newer framing: data-centre electricity demand roughly doubled from 485 TWh in 2025 to a projected 950 TWh in 2030, and AI-focused data-centre consumption is projected to triple over that period. The 945 TWh estimate belongs to the earlier 2025 base case; the 950 TWh figure is the 2026 outlook. Neither should be read as a certain outcome. The IEA cites constraints such as grid connections, energy-equipment supply chains and advanced chips, alongside uncertainty in demand.
Global share is not the whole grid story. In its 2025 base case, the IEA estimated that data centres would account for less than 10% of global electricity-demand growth from 2024 to 2030. Yet large facilities can still be difficult to integrate where their loads cluster geographically. A modest global share does not rule out pressure on particular local grids.
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Could networks, manufacturing or device replacement shift the impact?
Networks
Network electricity does not necessarily rise in proportion to AI traffic. The IEA describes the link between AI-related traffic and network energy use as uncertain. Fixed and core networks can use roughly the same energy regardless of traffic, while mobile-network energy also depends on coverage. The IEA judges a noticeable near-term effect from AI traffic unlikely compared with larger drivers of network traffic.
Hardware manufacturing and replacement
More AI-capable devices could bring indirect energy costs through manufacturing, and devices replaced sooner could add to manufacturing demand and e-waste. These are potential lifecycle effects, not a quantified global electricity total in the IEA material. Manufacturing energy is also distinct from electricity consumed while a device is running.
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- INSTALLS IN CIRCUIT PANEL of most homes with clamp-on sensors. Supports Single phase, Single-split phase, and 2-wire systems. 3-wire systems; 3-phase, 4-wire Wye systems with earthed (TN or TT) neutral (no-Delta) are supported with an additional 200A sensor (sold separately).
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- LOWER YOUR ELECTRIC BILL: Configure settings in the Emporia Energy App to automate energy management for time of use, peak demand, excess solar, and rewards programs. You can even see live reporting and invaluable savings opportunities instantly. Gauge real-time spending and get actionable notifications and automated energy management to help you reduce costs.
- REAL-TIME ENERGY DATA: REQUIRES 2.4 GHz WIFI WITH AN INTERNET CONNECTION to monitor energy use with iPhone / Android / Web app. Vue sensors collect energy data and are accurate from ±2%. The Vue is UL and CE Listed for your safety. 1 second data is only available in the app (when actively open) and retained 3 hours. Minute and hour data are retained in the cloud. 1 minute data is retained 7 days, 1 hour data is retained indefinitely. Export cloud data whenever you want in the app.
Local grid effects
Moving computation away from a central facility can distribute electricity use across more locations rather than remove it. Whether that eases or complicates grid demand depends on where the computing happens and how much equipment is deployed; a global net effect for edge AI has not been established.
How to assess an “AI uses more electricity” claim
Check what the comparison includes before accepting a total. A useful assessment should specify:
- The same workload: model, task and amount of computation being compared.
- Operational electricity: the device or server, and cooling or power overhead where measured.
- Utilization and batching: how heavily shared servers or local devices are used and whether work is grouped.
- Network requirements: data transferred, latency, coverage and whether traffic changes network capacity needs.
- Lifecycle boundary: manufacturing, expected hardware life, replacement and e-waste, kept distinct from operating electricity.
- Location and capability: where the electricity is drawn, and whether local processing’s privacy, latency or connectivity advantages justify its compute, storage and power limits.
Without those boundaries, a claim about one device, model or data centre cannot establish the net effect of moving AI workloads worldwide.
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