Network
Params
name
A human readable description of the object.
bandwidth_energy_intensity
Bandwith energy intensity of network in kilowatt_hour / gigabyte.
Backwards links
Calculated attributes
energy_footprint_per_job
Dictionary with EcoLogitsGenAIExternalAPIJob as keys and Generative ai model job energy footprint in network as values, in kilogram.
Example value: {
EcoLogitsGenAIExternalAPIJob Generative AI model job (4d5759): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in mg:
first 10 vals [0.127, 0.127, 0.0213, 0.0213, 0.149, 0.149, 0.0213, 0.149, 0.191, 0.0213],
last 10 vals [0.149, 0.0213, 0.0637, 0.0213, 0.149, 0.106, 0.127, 0.0637, 0.0637, 0.085],
VideoStreamingJob Video streaming job (79415b): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in g:
first 10 vals [23.8, 23.8, 3.97, 3.97, 27.8, 27.8, 3.97, 27.8, 35.7, 3.97],
last 10 vals [27.8, 3.97, 11.9, 3.97, 27.8, 19.8, 23.8, 11.9, 11.9, 15.9],
Job Manually defined job (74867f): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in kg:
first 10 vals [0.00000765, 0.00000765, 0.00000128, 0.00000128, 0.00000893, 0.00000893, 0.00000128, 0.000646, 0.00128, 0.00191],
last 10 vals [0.00319, 0.00318, 0.00319, 0.00319, 0.00319, 0.00255, 0.00191, 0.00127, 0.000636, 0],
GPUJob Manually defined GPU job (a9bd0f): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in mg:
first 10 vals [3.83, 3.83, 0.638, 0.638, 4.46, 4.46, 0.638, 4.46, 5.74, 0.638],
last 10 vals [4.46, 0.638, 1.91, 0.638, 4.46, 3.19, 3.83, 1.91, 1.91, 2.55],
}
Depends directly on:
- Hourly data transferred for Generative AI model job in usage pattern
- bandwith energy intensity of network
- Average carbon intensity of devices country
through the following calculations:
You can also visit the link to Generative AI model job energy footprint in network’s full calculation graph.
instances_fabrication_footprint
Example value: no value
Depends directly on:
through the following calculations:
You can also visit the link to no value’s full calculation graph.
energy_footprint
Hourly network energy footprint in kilogram.
Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in kg:
first 10 vals [0.0238, 0.0238, 0.00397, 0.00397, 0.0278, 0.0278, 0.00397, 0.0284, 0.037, 0.00588],
last 10 vals [0.00319, 0.00318, 0.00319, 0.00319, 0.00319, 0.00255, 0.00191, 0.00127, 0.000636, 0]
Depends directly on:
- Generative AI model job energy footprint in network
- Video streaming job energy footprint in network
- Manually defined job energy footprint in network
- Manually defined GPU job energy footprint in network
through the following calculations:
You can also visit the link to Hourly network energy footprint’s full calculation graph.
fabrication_impact_repartition_weight_sum
Example value: no value
Depends directly on:
through the following calculations:
You can also visit the link to Sum of network fabrication impact repartition weights’s full calculation graph.
usage_impact_repartition_weight_sum
Sum of network usage impact repartition weights in kilogram.
Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in kg:
first 10 vals [0.0238, 0.0238, 0.00397, 0.00397, 0.0278, 0.0278, 0.00397, 0.0284, 0.037, 0.00588],
last 10 vals [0.00319, 0.00318, 0.00319, 0.00319, 0.00319, 0.00255, 0.00191, 0.00127, 0.000636, 0]
Depends directly on:
- Generative AI model job energy footprint in network
- Video streaming job energy footprint in network
- Manually defined job energy footprint in network
- Manually defined GPU job energy footprint in network
through the following calculations:
You can also visit the link to Sum of network usage impact repartition weights’s full calculation graph.
usage_impact_repartition
Dictionary with EcoLogitsGenAIExternalAPIJob as keys and Network usage impact attribution to generative ai model job as values, in concurrent.
Example value: {
EcoLogitsGenAIExternalAPIJob Generative AI model job (4d5759): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000524, 0.00000517, 0.00000361],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
VideoStreamingJob Video streaming job (79415b): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [1, 1, 1, 1, 1, 1, 1, 0.977, 0.965, 0.675],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
Job Manually defined job (74867f): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.000321, 0.000321, 0.000321, 0.000321, 0.000321, 0.000321, 0.000321, 0.0227, 0.0347, 0.325],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
GPUJob Manually defined GPU job (a9bd0f): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.000161, 0.000161, 0.000161, 0.000161, 0.000161, 0.000161, 0.000161, 0.000157, 0.000155, 0.000108],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
}
Depends directly on:
through the following calculations:
You can also visit the link to network usage impact attribution to Generative AI model job’s full calculation graph.