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 (746a6e): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in mg:
first 10 vals [0.0425, 0.17, 0.191, 0.17, 0.0637, 0.0213, 0.149, 0.127, 0.191, 0.0425],
last 10 vals [0.0637, 0.17, 0.191, 0.0425, 0.17, 0.127, 0.106, 0.0425, 0.127, 0.0637],
VideoStreamingJob Video streaming job (3a2d35): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in g:
first 10 vals [7.93, 31.7, 35.7, 31.7, 11.9, 3.97, 27.8, 23.8, 35.7, 7.93],
last 10 vals [11.9, 31.7, 35.7, 7.93, 31.7, 23.8, 19.8, 7.93, 23.8, 11.9],
Job Manually defined job (f473c8): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in kg:
first 10 vals [0.00000255, 0.0000102, 0.0000115, 0.0000102, 0.00000383, 0.00000128, 0.00000893, 0.00000765, 0.000649, 0.00128],
last 10 vals [0.00319, 0.00319, 0.00318, 0.00319, 0.00319, 0.00319, 0.00255, 0.00191, 0.00127, 0.000636],
GPUJob Manually defined GPU job (ca3955): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in mg:
first 10 vals [1.28, 5.1, 5.74, 5.1, 1.91, 0.638, 4.46, 3.83, 5.74, 1.28],
last 10 vals [1.91, 5.1, 5.74, 1.28, 5.1, 3.83, 3.19, 1.28, 3.83, 1.91],
}
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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in kg:
first 10 vals [0.00794, 0.0317, 0.0357, 0.0317, 0.0119, 0.00397, 0.0278, 0.0238, 0.0363, 0.00921],
last 10 vals [0.00319, 0.00319, 0.00318, 0.00319, 0.00319, 0.00319, 0.00255, 0.00191, 0.00127, 0.000636]
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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in kg:
first 10 vals [0.00794, 0.0317, 0.0357, 0.0317, 0.0119, 0.00397, 0.0278, 0.0238, 0.0363, 0.00921],
last 10 vals [0.00319, 0.00319, 0.00318, 0.00319, 0.00319, 0.00319, 0.00255, 0.00191, 0.00127, 0.000636]
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 (746a6e): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000526, 0.00000462],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
VideoStreamingJob Video streaming job (3a2d35): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 0.982, 0.861],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
Job Manually defined job (f473c8): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0.000321, 0.000321, 0.000321, 0.000321, 0.000321, 0.000321, 0.000321, 0.000321, 0.0178, 0.139],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
GPUJob Manually defined GPU job (ca3955): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0.000161, 0.000161, 0.000161, 0.000161, 0.000161, 0.000161, 0.000161, 0.000161, 0.000158, 0.000138],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
}
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.