There is no defensible universal number of extra watts for software encoding a YouTube loop stream. The difference depends on your computer, encoder settings, scene, other activity and what your meter includes, so the useful answer comes from a controlled measurement on your own setup.
Measure average wall power with the same loop and streaming conditions, first without software encoding and then with it. Subtract the baseline from the streaming result; that difference is your measured increment for those conditions, not a figure to apply to another computer.
Why there is no universal extra-watts figure
“Software encoding” describes where the video-encoding work is performed, not a fixed amount of electricity. An encoder using the CPU on one machine may add a different amount of whole-computer power from the same encoder on another machine. Even on one computer, the result can change with the selected preset, output resolution, frame rate, scene and other work running at the same time.
The word “extra” also depends on the baseline. A computer that is already busy rendering graphics or running other applications starts from a different power draw than one doing little beyond playing a still image. A measurement taken at the wall includes the whole computer and its power supply, not just the CPU or encoder. If the monitor, speakers or router share the measured circuit, their draw is in the total too.
The sources reviewed for this topic do not report a matched experiment of a computer running a YouTube loop stream with and without software encoding. YouTube’s documentation explains its live-streaming and transcoding process, but does not publish a creator-side electricity figure. Its platform processing is separate from the electricity used by your streaming computer. See YouTube’s overview of live-streaming encoder settings and how YouTube processes live streams.
A published OBS recording experiment can help show why the result can be material, but it is not a typical-stream estimate. As discussed below, the researchers measured an approximately 100 W increase in a particular graphics-benchmark workload. That finding cannot tell you what a YouTube loop will add on your machine.
For a practical channel, the better question is: what is the average difference between two carefully matched runs at the settings you intend to use? The answer may be useful for planning electricity use or comparing encoding options, provided you record the conditions alongside it.
What changes encoder power use
The encoder and its settings shape the work. OBS describes x264 presets as a trade-off between CPU use and image quality: a preset that asks for more encoding work can change the computer’s load. Hardware encoding shifts encoding work to a specialised component, commonly in a GPU, but that does not by itself establish a lower total wall draw or identical visual quality. OBS’s hardware-encoding guidance explains the distinction and recommends considering performance. Test what your own system supports rather than assuming one method always wins.
Resolution and frame rate also matter because they affect the amount of video work to process. A stream at a higher resolution or frame rate is not a fair comparison against a lower setting if your aim is to isolate the encoder choice. Bitrate is another setting worth holding steady: changing it at the same time makes the result harder to interpret, even if its effect on total computer draw is not the main question.
The video itself may be simple or demanding to produce. A static devotional image with an audio track is not the same scene workload as animated backgrounds, multiple moving elements, transitions or overlays. A loop file that merely plays is also different from a live scene being composited in real time. Keep the scene and source material unchanged for each run. If your channel uses recurring footage or animation, the discussion of using the same background animation across a 24/7 lofi channel may help you think through what is actually being rendered.
Competing work can blur the result. A browser tab playing video, a file conversion, a game, a backup or an update may raise the baseline or interfere with encoding. GPU use can matter even when encoding is on the CPU, because the scene still has to be rendered and the system shares resources. OBS’s encoding-performance troubleshooting notes discuss workload and rendering factors that can affect performance.
Finally, the measurement boundary matters. A plug-in meter at the computer’s wall connection gives a whole-system figure; it cannot isolate the encoder’s power. That whole-system figure is usually the useful one for an electricity bill, because the bill is for the equipment drawing power, not for a chip in isolation. Be explicit about whether the reading includes a monitor, audio equipment or network gear.
Measure baseline and stream power consistently
A fair test is a small experiment, not a glance at a utility app while the computer is doing different things. Use a plug-in electricity meter or another suitable whole-system wall-power measurement. Follow the meter’s instructions and check that its rating is appropriate for the equipment connected. If the goal is the computer, measure the computer alone where practical. If your setup requires a shared circuit, leave the same equipment connected for every run.
Choose a representative loop file and a stable test period. Use the same computer, operating-system power mode, scene, audio, output resolution, frame rate, bitrate, network connection and background applications in each run. Avoid scheduling updates or other maintenance in the middle of the comparison. Let each condition settle before recording an average; a momentary peak or a reading taken during startup is not representative of a long-running stream.
Run a baseline condition first, with the loop or equivalent scene active but without the software-encoding stream. Then run the software-encoding condition with the stream configured at the settings you intend to use. If feasible, repeat the runs and compare the averages rather than relying on one short reading. Repetition helps reveal whether a difference is stable or merely normal variation in the computer’s activity.
Calculate the increment as:
average watts during software-encoding stream − average watts during matched baseline = measured extra watts
For example, if your meter’s two averages are A and B, subtract the baseline value from the stream value; do not substitute someone else’s watts for either reading. Record the measurement interval and conditions. If the result is negative or unexpectedly small, do not force it into a preferred conclusion: inspect whether the two conditions were genuinely matched, whether the system changed its behaviour, and whether the meter is displaying a stable average.
If you have a supported hardware encoder, a third run can help compare options. Keep output settings and acceptable picture quality as close as you can, then compare total wall watts, stream stability and the resulting image. OBS notes that hardware and software encoding can differ in quality at a given bitrate, so a watts comparison alone is not the whole decision. A useful starting point for setting up a repeatable loop is our guide to playing a long video without duplicate audio on YouTube Live.
Write down the computer, encoder and preset, resolution, frame rate, bitrate, scene complexity, duration and what the meter included. Without those details, “my stream adds this many watts” can be mistaken for a general rule. With them, the result is a practical estimate for your own channel and configuration.
Separate encoding from scene rendering and other work
A streaming application does more than encode a finished picture. It may decode the source video, composite layers into a scene, scale the output, mix audio and encode the result. Some of those tasks continue even if you switch from software to hardware encoding. That is why comparing a running stream against an idle desktop does not isolate encoding: it captures all the work added by the stream setup.
There are two useful comparisons, and they answer different questions. A matched baseline with the same loop and scene but no outgoing stream estimates the total extra power associated with running the stream workflow. A comparison between software and hardware encoding, with the same live scene and output settings, tells you which encoding path uses less whole-system power on that setup. Neither tells you the encoder chip’s isolated draw.
For the first comparison, preserve the scene and playback while disabling only the streaming/encoding activity where your software allows a meaningful baseline. If disabling the stream also stops playback or scene rendering, state that limitation: your result then measures the difference between two broader conditions. Avoid running unrelated tasks during either case. A recording test can be useful for a local comparison, but it is not automatically equivalent to a YouTube broadcast, since the network and application behaviour may differ.
If you also want to know whether a complex scene is the main burden, make a separate comparison: keep the encoder settings fixed and compare a simple scene with your normal scene. Do not combine that result with the software-versus-hardware test. Changing one factor at a time makes the cause of a difference clearer. OBS’s performance guidance is useful when checking dropped frames or rendering lag, but performance indicators are not substitutes for wall-power measurement.
YouTube performs its own processing after receiving a live stream so that viewers can watch in different formats and under different conditions. That platform-side work is not included in a meter connected to your computer and should not be added to the computer’s measured watts. For an overnight devotional or news loop, your local measurement remains about the equipment and settings you control.
If maintaining a local computer, its scene and the broadcast overnight is itself the concern, distinguish that operational problem from the electricity question. A workflow that sends an uploaded file to YouTube without keeping your computer running removes the need for that machine to encode continuously; StreamNeo addresses that specific always-on-computer burden by turning an uploaded video into a YouTube live stream. It does not change what YouTube does on its side, and it is YouTube-only.
What the OBS study does and does not show
A study by Simon Fraser University researchers, published in 2015, measured whole-system wall power while running the Heaven graphics benchmark and OBS. In their tested setup, the benchmark alone drew about 158 W at the wall. Adding OBS x264 recording at 30 FPS raised the reading by about 100 W, to around 250 W. These are reported figures for that computer and workload, not measurements of an unattended YouTube loop stream.
The useful lesson is bounded: software encoding can materially increase whole-system draw when it adds CPU work to an already demanding workload. The study does not establish how many watts encoding adds on a current computer, on a particular preset, or while streaming a static loop to YouTube. It does not provide a typical figure, nor a number to enter into an electricity-cost calculation for your own channel. You can read the researchers’ paper on bridging the gap between performance and energy.
One result in the paper also cautions against reading too much into a single setting. Its 60 FPS run used slightly less total power than its 30 FPS run; the researchers associated this with CPU encoding interfering with the benchmark and reducing GPU utilisation. That is not evidence that a higher frame rate usually saves electricity. It shows that interacting workloads can produce results that are not obvious from a setting label or one component’s utilisation.
A separate 2024 paper studied energy estimation for HEVC software CPU encoding and evaluated a model across its own encoding configurations. Its reported mean absolute percentage error is a model-performance figure within that study, not an uncertainty range for your meter or an estimate for YouTube streaming. It cannot be used to turn the OBS benchmark’s increase into a general streaming number.
Treat published examples as context for why measurement matters, not as a substitute for your own controlled test. Differences in hardware, software, workload and measurement boundary are exactly what make a watt figure easy to misapply.
Estimate energy cost from measured watts
Once you have a measured increment, you can estimate how much additional energy the encoding condition uses over a period. Multiply the extra watts by the number of hours, then divide by 1,000 to convert watt-hours to kilowatt-hours:
incremental kWh = measured extra watts × hours ÷ 1,000
This calculation estimates energy, not money. To estimate cost, multiply the incremental kWh by the per-unit electricity rate that applies to your account. Rates, taxes and billing arrangements vary, so use your current bill or supplier information rather than a generic price. The arithmetic should use the measured increment, not the whole computer’s power, if you want the additional energy attributable to the tested condition.
For a 24/7 channel, the relevant hours are the period you expect that exact setup to run. If you measure a shorter stable interval and use it to estimate a longer period, label the result an estimate: activity can vary across a day, and the test may not capture every restart, update or change in scene. If your stream uses several configurations, measure or estimate each separately rather than assuming one average applies to all of them.
Keep precision in proportion to the test. A wall meter’s displayed digits do not make a short, noisy run exact. Report the average, duration and conditions, and avoid presenting a calculated monthly cost as a guaranteed bill outcome. For a channel that runs a recorded playlist rather than a live composited scene, it can also help to consider whether the playback workflow itself is doing work that your proposed alternative would avoid. Our guide on keeping a recorded speedrun stream playing overnight covers that kind of always-on playback setup.
If the software-encoding increment is larger than you expected, first check whether the settings are necessary for the image your viewers need. Test a lower-complexity scene, a different supported encoder or a less demanding output setting one change at a time. Then confirm picture quality and stable delivery before choosing. The lowest reading is not automatically the best result if the stream becomes unreliable or the picture no longer suits the channel.
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FAQ
How many extra watts does software encoding add?
There is no defensible universal figure for a YouTube loop stream. The increment varies with the machine, encoder settings, scene, background activity and measurement boundary. Measure average wall power in matched baseline and software-stream conditions to get a figure for your own setup.
Does the OBS study mean my stream will use about 100 W more?
No. The roughly 100 W increase was measured in a 2015 OBS x264 recording experiment while a graphics benchmark was running on the researchers’ tested system. It is bounded context, not a YouTube loop result or a prediction for your computer.
Is hardware encoding always more efficient?
Not necessarily in total wall watts or at the same visual quality. Hardware encoding moves work to a specialised component, while software encoding uses CPU resources; compare both on your machine with matched output settings and check the resulting picture and stream stability.
Should I include YouTube’s transcoding in my electricity calculation?
No, not in a measurement of your computer’s wall power. YouTube’s platform-side processing is separate from the creator-side equipment measured at your wall connection. Estimate local energy from the measured watts and hours, then apply the electricity rate relevant to your bill.