Model Comparisons

The following table shows the different electricity consumption of popular NLP and Computer visions models

Electricity consumption of AI cloud instance

Model

GPU

Training Time (H)

Consumption (kWh)

BERTfintetune

4 V100

6

3.1

BERTpretrain

8 V100

36

37.3

6BTransf.

256 A100

192

13 812.4

Dense121

1 P40

0.3

0.02

Dense169

1 P40

0.3

0.03

Dense201

1 P40

0.4

0.04

ViTTiny

1 V100

19

1.7

ViTSmall

1 V100

19

2.2

ViTBase

1 V100

21

4.7

ViTLarge

4 V100

90

93.3

ViTHuge

4 V100

216

237.6

Impact of time of year and region

Carbon emissions that would be emitted from training BERT (language modeling on 8 V100s for 36 hours) in different locations:

Models emissions comparison

In this case study, time of year might not be relevant in most cases, but localisation can have a great impact on carbon emissions.

Here, and in the graph below, emissions equivalent are estimated using Microsoft Azure cloud tools. CodeCarbon has developed its own measuring tools. The result could be different.

Comparisons

Emissions for the 11 described models can be displayed as below:

Models emissions comparison

The black line represents the average emissions (across regions and time of year). The light blue represents the first and fourth quartiles. On the right side, equivalent sources of emissions are displayed as comparison points (source : US Environmental Protection Agency). NB : presented on a log scale

References

Measuring the Carbon intensity of AI in Cloud Instance

Another source comparing models carbon intensity: Energy and Policy Considerations for Deep Learning in NLP