EcoLogitsGenAIExternalAPIServer
Virtual server backing an EcoLogitsGenAIExternalAPI. Aggregates the per-request fabrication and energy footprints emitted by the underlying EcoLogits model into hourly footprints.
Params
name
A human readable description of the object.
Backwards links
Calculated attributes
instances_fabrication_footprint
Hourly fabrication-phase footprint of the model server, equal to per-request embodied GWP times hourly request count, summed over jobs.
Example value: 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 [1130, 322, 483, 161, 805, 1130, 966, 644, 1290, 805],
last 10 vals [966, 483, 483, 644, 161, 805, 322, 966, 483, 805]
Depends directly on:
through the following calculations:
You can also visit the link to Instances fabrication footprint for claude-opus-4-5’s full calculation graph.
instances_energy
Hourly energy consumed by the model server, equal to per-request energy times hourly request count, summed over jobs.
Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in Wh:
first 10 vals [34.1, 9.75, 14.6, 4.88, 24.4, 34.1, 29.3, 19.5, 39, 24.4],
last 10 vals [29.3, 14.6, 14.6, 19.5, 4.88, 24.4, 9.75, 29.3, 14.6, 24.4]
Depends directly on:
through the following calculations:
You can also visit the link to Instances energy for claude-opus-4-5’s full calculation graph.
energy_footprint
Hourly energy-use footprint of the model server, equal to per-request usage GWP times hourly request count, summed over jobs.
Example value: 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 [13.1, 3.74, 5.61, 1.87, 9.35, 13.1, 11.2, 7.48, 15, 9.35],
last 10 vals [11.2, 5.61, 5.61, 7.48, 1.87, 9.35, 3.74, 11.2, 5.61, 9.35]
Depends directly on:
through the following calculations:
You can also visit the link to Energy footprint for claude-opus-4-5’s full calculation graph.
fabrication_impact_repartition_weights
Per-job weights used to attribute the model server's fabrication footprint, proportional to per-request embodied GWP times hourly request volume.
Example value: {
EcoLogitsGenAIExternalAPIJob Generative AI model job (f7b526): 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 [1130, 322, 483, 161, 805, 1130, 966, 644, 1290, 805],
last 10 vals [966, 483, 483, 644, 161, 805, 322, 966, 483, 805],
}
Depends directly on:
through the following calculations:
You can also visit the link to Generative AI model job fabrication weight in impact repartition’s full calculation graph.
fabrication_impact_repartition_weight_sum
Sum of fabrication impact repartition weights, used as the denominator when normalising into per-container shares.
Example value: 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 [1130, 322, 483, 161, 805, 1130, 966, 644, 1290, 805],
last 10 vals [966, 483, 483, 644, 161, 805, 322, 966, 483, 805]
Depends directly on:
through the following calculations:
You can also visit the link to Fabrication impact repartition weights sum’s full calculation graph.
fabrication_impact_repartition
Normalised share of fabrication-phase emissions that this object attributes to each container.
Example value: {
EcoLogitsGenAIExternalAPIJob Generative AI model job (f7b526): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
}
Depends directly on:
- Generative AI model job fabrication weight in impact repartition
- Fabrication impact repartition weights sum
through the following calculations:
You can also visit the link to fabrication impact attribution to Generative AI model job’s full calculation graph.
usage_impact_repartition_weights
Per-job weights used to attribute the model server's energy-use footprint, proportional to per-request usage GWP times hourly request volume.
Example value: {
EcoLogitsGenAIExternalAPIJob Generative AI model job (f7b526): 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 [13.1, 3.74, 5.61, 1.87, 9.35, 13.1, 11.2, 7.48, 15, 9.35],
last 10 vals [11.2, 5.61, 5.61, 7.48, 1.87, 9.35, 3.74, 11.2, 5.61, 9.35],
}
Depends directly on:
through the following calculations:
You can also visit the link to Generative AI model job usage weight in impact repartition’s full calculation graph.
usage_impact_repartition_weight_sum
Sum of usage impact repartition weights, used as the denominator when normalising into per-container shares.
Example value: 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 [13.1, 3.74, 5.61, 1.87, 9.35, 13.1, 11.2, 7.48, 15, 9.35],
last 10 vals [11.2, 5.61, 5.61, 7.48, 1.87, 9.35, 3.74, 11.2, 5.61, 9.35]
Depends directly on:
through the following calculations:
You can also visit the link to Usage impact repartition weights sum’s full calculation graph.
usage_impact_repartition
Normalised share of usage-phase emissions that this object attributes to each container.
Example value: {
EcoLogitsGenAIExternalAPIJob Generative AI model job (f7b526): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
}
Depends directly on:
through the following calculations:
You can also visit the link to usage impact attribution to Generative AI model job’s full calculation graph.