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EcoLogitsGenAIExternalAPIJob

One inference call against an EcoLogitsGenAIExternalAPI, sized by the average output token count. Per-request energy and embodied GWP are computed from the EcoLogits impact DAG.

Params

name

A human readable description of the object.

external_api

EcoLogitsGenAIExternalAPI the call is routed to.

An instance of EcoLogitsGenAIExternalAPI.

output_token_count

Average number of tokens generated per call. Drives generation latency, energy use, and embodied GWP through the EcoLogits impact model.

Unit: dimensionless.

data_stored

Unit: megabyte_stored.

Fixed by EcoLogitsGenAIExternalAPIJob to 0.0 MB_stored — not configurable.

compute_needed

Unit: cpu_core.

Fixed by EcoLogitsGenAIExternalAPIJob to 0.0 cpu_core — not configurable.

ram_needed

Unit: megabyte_ram.

Fixed by EcoLogitsGenAIExternalAPIJob to 0.0 MB_ram — not configurable.

Calculated attributes

data_transferred

Data transferred per call, estimated as 5 bytes per token (4 bytes UTF-8 plus 1 byte JSON overhead) times the output token count.

Example value: 5 kB

Depends directly on:

through the following calculations:

You can also visit the link to Data transferred for claude-opus-4-5’s full calculation graph.

impacts

Cached EcoLogits impact dictionary for one call, computed from the model parameters, output token count, and grid carbon intensity. Subsequent updates extract individual fields from this dictionary.

Example value: {'model_active_parameter_count': 133.5, 'model_total_paramet...

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits impacts’s full calculation graph.

gpu_energy

Extracts the gpu_energy field from the cached EcoLogits impact dictionary on this job, converted into a typed e-footprint quantity.

Example value: 117 mWh

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits gpu_energy for claude-opus-4-5’s full calculation graph.

generation_latency

Extracts the generation_latency field from the cached EcoLogits impact dictionary on this job, converted into a typed e-footprint quantity.

Example value: 30.9 s

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits generation_latency for claude-opus-4-5’s full calculation graph.

model_required_memory

Extracts the model_required_memory field from the cached EcoLogits impact dictionary on this job, converted into a typed e-footprint quantity.

Example value: 1.61 TB ram

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits model_required_memory for claude-opus-4-5’s full calculation graph.

gpu_required_count

Extracts the gpu_required_count field from the cached EcoLogits impact dictionary on this job, converted into a typed e-footprint quantity.

Example value: 32

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits gpu_required_count for claude-opus-4-5’s full calculation graph.

server_energy

Extracts the server_energy field from the cached EcoLogits impact dictionary on this job, converted into a typed e-footprint quantity.

Example value: 644 mWh

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits server_energy for claude-opus-4-5’s full calculation graph.

request_energy

Extracts the request_energy field from the cached EcoLogits impact dictionary on this job, converted into a typed e-footprint quantity.

Example value: 4.88 Wh

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits request_energy for claude-opus-4-5’s full calculation graph.

request_usage_gwp

Extracts the request_usage_gwp field from the cached EcoLogits impact dictionary on this job, converted into a typed e-footprint quantity.

Example value: 1.87 g

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits request_usage_gwp for claude-opus-4-5’s full calculation graph.

server_gpu_embodied_gwp

Extracts the server_gpu_embodied_gwp field from the cached EcoLogits impact dictionary on this job, converted into a typed e-footprint quantity.

Example value: 31.5 t

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits server_gpu_embodied_gwp for claude-opus-4-5’s full calculation graph.

request_embodied_gwp

Extracts the request_embodied_gwp field from the cached EcoLogits impact dictionary on this job, converted into a typed e-footprint quantity.

Example value: 161 mg

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits request_embodied_gwp for claude-opus-4-5’s full calculation graph.

request_duration

Request duration of one call, equal to the generation latency derived from EcoLogits.

Example value: 30.9 s

Depends directly on:

through the following calculations:

You can also visit the link to Ecologits generation_latency for claude-opus-4-5’s full calculation graph.

hourly_occurrences_per_usage_pattern

Hourly count of job invocations broken down by usage pattern, derived from when each usage pattern's journeys start and at what point in the journey this job is triggered.

Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [7, 2, 3, 1, 5, 7, 6, 4, 8, 5],
last 10 vals [6, 3, 3, 4, 1, 5, 2, 6, 3, 5],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly occurrences in usage pattern’s full calculation graph.

hourly_avg_occurrences_per_usage_pattern

Hourly count of job invocations averaged with respect to job duration, so a job that runs longer than an hour contributes a fractional occurrence to several modeling buckets.

Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [0.0601, 0.0172, 0.0258, 0.00859, 0.0429, 0.0601, 0.0515, 0.0344, 0.0687, 0.0429],
last 10 vals [0.0515, 0.0258, 0.0258, 0.0344, 0.00859, 0.0429, 0.0172, 0.0515, 0.0258, 0.0429],
}

Depends directly on:

through the following calculations:

You can also visit the link to Average hourly occurrences in usage pattern’s full calculation graph.

hourly_data_transferred_per_usage_pattern

Hourly volume of data transferred over the network by this job, broken down by usage pattern.

Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in kB:
first 10 vals [35, 10, 15, 5, 25, 35, 30, 20, 40, 25],
last 10 vals [30, 15, 15, 20, 5, 25, 10, 30, 15, 25],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly data transferred in usage pattern’s full calculation graph.

hourly_data_stored_per_usage_pattern

Hourly net change in storage volume caused by this job, broken down by usage pattern.

Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in B stored:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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 Hourly data stored in usage pattern’s full calculation graph.

hourly_avg_occurrences_across_usage_patterns

Total hourly count of duration-averaged job invocations summed over every usage pattern.

Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [0.0601, 0.0172, 0.0258, 0.00859, 0.0429, 0.0601, 0.0515, 0.0344, 0.0687, 0.0429],
last 10 vals [0.0515, 0.0258, 0.0258, 0.0344, 0.00859, 0.0429, 0.0172, 0.0515, 0.0258, 0.0429]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly average occurrences across usage patterns’s full calculation graph.

hourly_data_transferred_across_usage_patterns

Total hourly volume of data transferred over the network by this job, summed over every usage pattern.

Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in kB:
first 10 vals [35, 10, 15, 5, 25, 35, 30, 20, 40, 25],
last 10 vals [30, 15, 15, 20, 5, 25, 10, 30, 15, 25]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly data transferred across usage patterns’s full calculation graph.

hourly_data_stored_across_usage_patterns

Total hourly net change in storage volume caused by this job, summed over every usage pattern.

Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in B stored:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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 Hourly data stored across usage patterns’s full calculation graph.

fabrication_impact_repartition_weights

Weights used to attribute fabrication-phase emissions of upstream impact sources to each container of this object.

Example value: {
UsageJourneyStep 20 min streaming (264451): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [2.33, 0.667, 1, 0.333, 1.67, 2.33, 2, 1.33, 2.67, 1.67],
last 10 vals [2, 1, 1, 1.33, 0.333, 1.67, 0.667, 2, 1, 1.67],
}

Depends directly on:

through the following calculations:

You can also visit the link to 20 min streaming 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 :
first 10 vals [2.33, 0.667, 1, 0.333, 1.67, 2.33, 2, 1.33, 2.67, 1.67],
last 10 vals [2, 1, 1, 1.33, 0.333, 1.67, 0.667, 2, 1, 1.67]

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: {
UsageJourneyStep 20 min streaming (264451): 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 fabrication impact attribution to 20 min streaming’s full calculation graph.

usage_impact_repartition_weights

Weights used to attribute usage-phase emissions of upstream impact sources to each container of this object.

Example value: {
UsageJourneyStep 20 min streaming (264451): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in ·g/kWh:
first 10 vals [198, 56.7, 85, 28.3, 142, 198, 170, 113, 227, 142],
last 10 vals [170, 85, 85, 113, 28.3, 142, 56.7, 170, 85, 142],
}

Depends directly on:

through the following calculations:

You can also visit the link to 20 min streaming 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/kWh:
first 10 vals [198, 56.7, 85, 28.3, 142, 198, 170, 113, 227, 142],
last 10 vals [170, 85, 85, 113, 28.3, 142, 56.7, 170, 85, 142]

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: {
UsageJourneyStep 20 min streaming (264451): 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 20 min streaming’s full calculation graph.

hourly_occurrences_across_usage_patterns

Hourly count of occurrences of this job summed across all usage patterns that trigger it.

Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [7, 2, 3, 1, 5, 7, 6, 4, 8, 5],
last 10 vals [6, 3, 3, 4, 1, 5, 2, 6, 3, 5]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly occurrences across usage patterns’s full calculation graph.