Energy consumption comparison between different graphics settings in GTA V

Seyidali Bulut, Johan van den Berg, Michal Kuchar, Artin Sanaye.

Group 20.

This experiment analyses the energy consumption of GTA V under different graphics settings to determine the increase in energy usage from low to high settings. To ensure our results are not dependent on a single computer, we use multiple machines to run the in-game integrated benchmark test 15 times for each graphics setting. The collected data highlights the differences in energy consumption across various graphical configurations.

Introduction

The increased use of computers has led to a rise in global energy consumption. It is estimated that software will account for 51% of all electricity usage as early as 2030. This means that software will be responsible for 23% of global greenhouse gas emissions in the coming decades (Andrae & Edler, 2015). As a result, software companies are placing greater emphasis on sustainability to create long-term value without harming the environment.

A branch of existing software systems that is constantly growing is the gaming industry, with about 2.6 billion global gamers and gaming titles available on almost every digital device, making video games one of the most popular and enduring hobbies worldwide (Topic: Video Gaming Worldwide, 2024). To satisfy this massive player base, companies strive to develop games with greater realism, improved physics engines, and enhanced graphics, all of which require more computational power. This is evident in the gaming industry, where new games demand increasingly advanced GPUs as minimum requirements. Despite this vast player base, the gaming industry has not been as proactive in addressing environmental sustainability as other computing technology sectors (Pérez et al., 2024).

As a result of the ever-growing impact of the gaming industry on the environment, we decided to investigate the difference in energy consumption for high graphics settings in games in comparison to low graphics settings. To achieve this goal, we decided to measure the difference in energy usage within a single game at different graphics settings. We selected GTA V, which still has 30 million active monthly players 12 years after its release (Xoob & Xoob, 2024). This game was not only selected for its relevance, but also because it is playable over a wide array of graphics settings, supporting both modern, powerful GPUs and lower-tier, older GPUs.

To ensure the reproducibility of the experiment, we used the game’s built-in benchmark test across multiple computers. Our results show that there is a double to quadruple difference in power consumption when the graphics settings are increased for a slight improvement in graphics quality. Therefore, we propose solutions to reduce power consumption and lower gaming’s environmental impact.

Methodology

During the experiment, it was crucial to measure the energy consumption of the selected game as accurately as possible while minimizing interference from other background programs to ensure reproducility across different computers. To achieve this, we closed all unnecessary programs, keeping only essential background services running.

Furthermore, we warmed up the system before starting the experiment and allowed cooldown periods between runs. This was achieved by running a non-recorded warm-up benchmark test before data collection, and restarting the game between runs which takes about a minute, ensuring proper cooldown.

We compared two different graphics settings, classified as a low graphics quality run and a high graphics quality run. On each computer, we ran the benchmark test 15 times for both the low and high settings, totaling 30 runs per PC. Initially, we considered running a higher number of tests, but given that each benchmark test takes approximately 3 minutes per run, we realized this would require a significant amount of time and we were unable to find a way to automate the measurements. Fortunately, our measurement software captured samples every second during the 3 minute benchmark, so this can be seen as 15 sets of hundreds of measurements per case, rather than just 15 measurements per case.

To ensure reliable results, we conducted the same experiments on different PCs. This allowed us to compare the results and determine whether the difference in energy consumption on a single PC was consistent across multiple systems. To that effect, Vsync was turned on for both graphics settings. Vsync caps the framerate at the monitor refresh rate (60hz in our case). If this setting is left off, the game will try to render as many frames as possible, which means the GPU will use maximal power regardless of graphics settings.

Graphics Settings

The number of available options for each graphics setting varies, ranging from 2 to 6 choices. Most settings have options such as Normal, High, and Very High, while some also include Ultra and others are limited to simple On-Off options.

To ensure a clear difference between runs, settings with On-Off options were set to Off for the low graphics run and On for the high graphics run. For settings with more than two options, we selected the lowest computationally demanding option (Normal) for the low run and increased it by two levels for the high run, which was typically Very High.

This selection method was designed to create a noticeable difference in power consumption that could be reliably detected. The specific settings used are included in Appendix A to ensure the replicability of our results.

Experiment procedure

Our experiment procedure is outlined below. The experiment first has a startup procedure, followed by a segment to be repeated 15 times for each setting.

Ensure notifications are turned off
Everything closed (including background applications) except for the Rockstar Launcher and GPU-Z. Running background services (indicated by taskbar icons) are limited to Windows Defender, the Nvidia app and the audio driver.
Only connected devices are a keyboard, mouse and a monitor.
The computer is connected to the internet using an ethernet cable (GTA V needs an internet connection to launch).
Power mode set to “best performance” in Windows.
Start GTA V
Run benchmark test without measurement for warm-up

Repeat 15 times for low settings and 15 times for high settings:

Start a new logging file in GPU-Z with appropriate naming
Run benchmark test
GTA V restarts automatically after benchmark, allowing for a cooldown period

Data collection

During the course, EnergiBridge was recommended. We found EnergiBridge to be unsuitable for our purposes as it is less focused on measuring GPU power consumption and therefore started looking for alternative software. We decided instead to use GPU-Z, which records GPU metrics. This fits our purposes as we are running a GPU intensive task. While GPU-Z did not seem to be giving accurate measurements of GPU power consumption, it did provide other values from which we were able to estimate power consumption.

According to Reddit user Blandbl on the /r/overclocking forum, the equation that describes power consumption of a GPU is given by $P = \alpha C V^2 f$, where $P$ is power in Watts, $\alpha$ is the activity factor (GPU utilization percentage), $V$ is the voltage, $f$ is the frequency (clock speed) of the GPU, and $C$ is capacitance.

This expression is seemingly based on the equation for dynamic power consumption of a CPU. It assumes all transistors are active at the same time. The GPU version with the activity factor $\alpha$ takes transistor activity into account.

Assuming that capacitance is an inherent property of a GPU and therefore constant, we estimated power consumption using $\alpha V^2 f$, since all underlying quantites are measured by GPU-Z. This is equivalent to $P/C$, which should be directly proportional to $P$ assuming constant $C$. We refer to this $P/C$ quantity as the “power scalar”, and report relative differences between low and high settings per computer.

Hardware/Software Details

The specifications of the computers used in the experiments are listed below.

Computer 1:

Computer 2:

Computer 3:

Results

The GPU-Z log files gave us time-series data, from which we calculated the power scalar over time, using the expression stated earlier. The figure below shows an example measurement from several runs of the benchmark. The jumps between low and high power scalar values between scenes of the benchmark can be clearly seen.

Time series of power scalars

Notable is the fact that the graph for Computer 3 does not show the high settings benchmark until the end, this is because the computer crashed at this point in the benchmark. This was not completely unexpected as this game was not made to run on systems without a dedicated GPU.

To isolate the distrbutions of the high power scalar values, we aggregated the measurements from all 15 runs per computer per settings configuration, and plotted their histograms. Below is an example of two such histograms. One corresponds to low settings and shows a bimodal distribution - one peak corresponds to low power scalar measurements when the GPU was inactive between benchmark scenes, and the other peak corresponds to high power scalar measurements when the GPU was active during the benchmark scenes. The histogram corresponding to high settings tells a similar story, except the second peak is at a higher power scalar value, since the GPU was consuming more power rendering the game at a higher graphics quality.

Bimodal power scalar distributions

Since this bimodal trend emerged in measurements from all computers, we decided to use this histogram technique to isolate the distrbutions where the GPU was active. Below is an example - the high settings histogram above zoomed in on the active GPU distribution.

Power scalar distribution with active GPU

Notably, none of the measurements appeared normally distributed, despite a large number of samples. This was confirmed for all distributions using the Shapiro-Wilk test - the figure above has a p-value of $3.88 \times 10^{-27}$. This is not surprising, because we observed our GPUs quickly trending towards specific frequency, voltage and utilization combinations under load, and deviating only slightly higher during more intense parts of the benchmark. In a following section, we will use the Mann-Whitney (which does not assume normal distributions) test to evaluate statistical significance between low and high settings.

Analysis

The following table compares means, standard errors, test statistics and relative changes for all 3 computers. Note that a Mann-Whitney p-value of 0 indicates a value so low that it is inndistinguishable from 0 at machine precision.

	Mean power scalar low settings	Mean power scalar high settings	Mann-Whitney p-value	High/low percent change
Computer 1	$197.020 \pm 0.521$	$348.448 \pm 1.310$	$0$	76.859
Computer 2	$289.282 \pm 1.913$	$1040.533 \pm 1.974$	$0$	259.695
Computer 3	$1090.662 \pm 1.353$	$1151.618 \pm 0.291$	$4.35 \times 10^{-43}$	5.589

For a more detailed look, the figures below compare means and spreads of the low settings case vs. the high settings case for each computer.

Computer 1

Settings comparison PC1

Computer 2

Settings comparison PC2

Computer 3

Settings comparison PC3

Statistical significance

By empirical analysis alone, we can see in the figures above that the distributions of power scalars are vastly different in all 3 computers. To confirm that the differences are statistically significant, we see in the table above that the Mann-Whitney p-values are small enough for any $\alpha$ threshold.

Practical significance

In all computers except Computer 3, we observe a power scalar increase from almost double to almost quadruple. Under the assumption that $C$ is constant (see Data collection), this should indicate an almost double to almost quadruple increase in power consumption. Looking at this comparison of GTA V graphics quality under different settings, it cannot really be said that high settings look even close to “twice as good” as low settings. There seem to be diminishing returns, making the high settings not worth it if power consumption is a concern.

Discussion

In the analysis, a clear statistical difference between power consumption at high and low settings was found for all computers. Interestingly, even though the integrated graphics card of Computer 3 was already running basically at maximum capacity on low settings, there was still a statistical difference with the experiments at high settings. Most relevant to real-life usage of GTA V are computers 1 and 2. The tests on these systems have shown that high settings lead to between double and quadruple the power of lower graphic settings. The higher power consumption of the 2060 in comparison to the 1070 in our results align with those of a test (also with Vsync on) at techpowerup.com.

These results lead us to conclude that gaming at higher settings has a higher environmental impact than gaming at lower settings. Therefore we would like to urge gamers to use the lowest graphic settings acceptable to them to limit their carbon footprint. Having said this, the best thing to do is to not buy the PC at all, since only around 15% of the emissions actually come from the usage of a PC over a 6 year period and a staggering 85% from manufacture.

Lastly, in the gaming world there is a big dislike for using Vsync (try looking up “vsync on or off”), which is interesting as it stops screen tearing and you cannot see any frames produced faster than the refresh rate of your monitor anyway. If Vsync is not used, power consumption in most cases will increase dramatically, since the computer will try to render as many frames as possible. For more sustainable gaming, Vsync should be turned on, as in most cases it does not affect gameplay, but does affect power consumption.

Limitations

Some limitations of our experiments are discussed briefly in this section.

Experiments limited to a single game

It would be better to run experiments using different games, as this would give a more complete overview of differences in energy consumption between low and high settings across different games. However, due to time constraints this was not possible and this is left as future work.

Low number of experiments

For optimal results it would be desirable to have more than three computers run the experiments. Additionally, for optimal experiments, one would want to run the experiment using only systems capable of running GTA V in a manner in which it is playable, as this better represents the actual use case of the software and not just one in an experimental setting.

Only measuring GPU power consumption

To get a more realistic picture of the total power consumption of a game, it is desirable to measure the energy consumption of the entire computer while running the game. However, doing so would require a physical device to measure the power consumed by the computer at the wall outlet. This was not available to us and therefore it was decided to only measure GPU power consumption.

Future work

For additional research, it would be interesting to learn more about the environmental impact of gaming by conducting our experiment across different games. Conducting such experiments will help us understand the environmental impact of the gaming industry better and could inspire game studios to take such research into account when developing their games.

Conclusion

The GPU power consumption of GTA V was tested and analysed in this project. The tests were conducted using 3 computers, two desktops with a dedicated GPU and one with an integrated GPU. On all systems the built in benchmark test was run 15 times for both low settings and high settings. From this experiment we found that for systems capable of running beyond both graphic settings the power consumption either doubled or quadrupled for high settings compared to low settings. Therefore, we recommend using lower graphic settings in combination with vsync to limit the framerate for gaming with as little environmental impact as possible.

Appendix

A: Graphics Settings

Low Run

High Run