EcoLogitsGenAIExternalAPIJob

Params

name

A human readable description of the object.

external_api

An instance of EcoLogitsGenAIExternalAPI.

output_token_count

Output token count for claude-opus-4-5 in dimensionless.

Backwards links

• UsageJourneyStep

Calculated attributes

data_transferred

ExplainableQuantity in byte, representing the Data transferred for claude-opus-4-5.

Example value: 5000.0 byte

Depends directly on:

• Output token count for claude-opus-4-5 from e-footprint hypothesis

through the following calculations:

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

impacts

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

Depends directly on:

• claude-opus-4-5 active parameter count (in billions) from Ecologits llm_impacts function
• claude-opus-4-5 total parameter count (in billions) from Ecologits llm_impacts function
• Output token count for claude-opus-4-5 from e-footprint hypothesis
• Average carbon intensity of electricity mix for anthropic from Ecologits llm_impacts function

through the following calculations:

You can also visit the link to Ecologits impacts for Generative AI model job from Ecologits compute_llm_impacts_dag function’s full calculation graph.

gpu_energy

ExplainableQuantity in kilowatt_hour, representing the Ecologits gpu_energy for claude-opus-4-5 from ecologits.

Example value: 0.0 kilowatt_hour

Depends directly on:

• Ecologits impacts for Generative AI model job from Ecologits compute_llm_impacts_dag function

through the following calculations:

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

generation_latency

ExplainableQuantity in second, representing the Ecologits generation_latency for claude-opus-4-5 from ecologits.

Example value: 130.02 second

Depends directly on:

• Ecologits impacts for Generative AI model job from Ecologits compute_llm_impacts_dag function

through the following calculations:

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

model_required_memory

ExplainableQuantity in gigabyte, representing the Ecologits model_required_memory for claude-opus-4-5 from ecologits.

Example value: 1608.0 gigabyte

Depends directly on:

• Ecologits impacts for Generative AI model job from Ecologits compute_llm_impacts_dag function

through the following calculations:

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

gpu_required_count

ExplainableQuantity in dimensionless, representing the Ecologits gpu_required_count for claude-opus-4-5 from ecologits.

Example value: 32.0 dimensionless

Depends directly on:

• Ecologits impacts for Generative AI model job from Ecologits compute_llm_impacts_dag function

through the following calculations:

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

server_energy

ExplainableQuantity in kilowatt_hour, representing the Ecologits server_energy for claude-opus-4-5 from ecologits.

Example value: 0.0 kilowatt_hour

Depends directly on:

• Ecologits impacts for Generative AI model job from Ecologits compute_llm_impacts_dag function

through the following calculations:

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

request_energy

ExplainableQuantity in kilowatt_hour, representing the Ecologits request_energy for claude-opus-4-5 from ecologits.

Example value: 0.01 kilowatt_hour

Depends directly on:

• Ecologits impacts for Generative AI model job from Ecologits compute_llm_impacts_dag function

through the following calculations:

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

request_usage_gwp

ExplainableQuantity in kilogram, representing the Ecologits request_usage_gwp for claude-opus-4-5 from ecologits.

Example value: 0.0 kilogram

Depends directly on:

• Ecologits impacts for Generative AI model job from Ecologits compute_llm_impacts_dag function

through the following calculations:

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

server_gpu_embodied_gwp

ExplainableQuantity in kilogram, representing the Ecologits server_gpu_embodied_gwp for claude-opus-4-5 from ecologits.

Example value: 28048.0 kilogram

Depends directly on:

• Ecologits impacts for Generative AI model job from Ecologits compute_llm_impacts_dag function

through the following calculations:

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

request_embodied_gwp

ExplainableQuantity in kilogram, representing the Ecologits request_embodied_gwp for claude-opus-4-5 from ecologits.

Example value: 0.0 kilogram

Depends directly on:

• Ecologits impacts for Generative AI model job from Ecologits compute_llm_impacts_dag function

through the following calculations:

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

request_duration

ExplainableQuantity in second, representing the Ecologits generation_latency for claude-opus-4-5 from ecologits.

Example value: 130.02 second

Depends directly on:

• Ecologits generation_latency for claude-opus-4-5 from Ecologits

through the following calculations:

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

hourly_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly generative ai model job occurrences in usagepattern usage pattern as values, in occurrence.

Example value: {
c374c9: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in occurrence:
first 10 vals [2.0, 4.0, 4.0, 8.0, 4.0, 2.0, 3.0, 6.0, 2.0, 6.0],
last 10 vals [4.0, 3.0, 7.0, 6.0, 3.0, 5.0, 6.0, 4.0, 5.0, 4.0],
}

Depends directly on:

• usage pattern UTC

through the following calculations:

You can also visit the link to Hourly Generative AI model job occurrences in UsagePattern usage pattern’s full calculation graph.

hourly_avg_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and Average hourly generative ai model job occurrences in usage pattern as values, in concurrent.

Example value: {
c374c9: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent:
first 10 vals [0.07, 0.14, 0.14, 0.29, 0.14, 0.07, 0.11, 0.22, 0.07, 0.22],
last 10 vals [0.14, 0.11, 0.25, 0.22, 0.11, 0.18, 0.22, 0.14, 0.18, 0.14],
}

Depends directly on:

• Hourly Generative AI model job occurrences in UsagePattern usage pattern
• Ecologits generation_latency for claude-opus-4-5 from Ecologits

through the following calculations:

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

hourly_data_transferred_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly data transferred for generative ai model job in usage pattern as values, in megabyte.

Example value: {
c374c9: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in MB:
first 10 vals [0.01, 0.02, 0.02, 0.04, 0.02, 0.01, 0.02, 0.03, 0.01, 0.03],
last 10 vals [0.02, 0.02, 0.04, 0.03, 0.02, 0.02, 0.03, 0.02, 0.02, 0.02],
}

Depends directly on:

• Average hourly Generative AI model job occurrences in usage pattern
• Data transferred for claude-opus-4-5
• Ecologits generation_latency for claude-opus-4-5 from Ecologits

through the following calculations:

You can also visit the link to Hourly data transferred for Generative AI model job in usage pattern’s full calculation graph.

hourly_data_stored_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly data stored for generative ai model job in usage pattern as values, in megabyte.

Example value: {
c374c9: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in MB:
first 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
last 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
}

Depends directly on:

• Average hourly Generative AI model job occurrences in usage pattern
• Data stored by request Generative AI model job from e-footprint hypothesis
• Ecologits generation_latency for claude-opus-4-5 from Ecologits

through the following calculations:

You can also visit the link to Hourly data stored for Generative AI model job in usage pattern’s full calculation graph.

hourly_avg_occurrences_across_usage_patterns

Hourly generative ai model job average occurrences across usage patterns in concurrent.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent:
first 10 vals [0.07, 0.14, 0.14, 0.29, 0.14, 0.07, 0.11, 0.22, 0.07, 0.22],
last 10 vals [0.14, 0.11, 0.25, 0.22, 0.11, 0.18, 0.22, 0.14, 0.18, 0.14]

Depends directly on:

• Average hourly Generative AI model job occurrences in usage pattern

through the following calculations:

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

hourly_data_transferred_across_usage_patterns

Hourly generative ai model job data transferred across usage patterns in megabyte.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in MB:
first 10 vals [0.01, 0.02, 0.02, 0.04, 0.02, 0.01, 0.02, 0.03, 0.01, 0.03],
last 10 vals [0.02, 0.02, 0.04, 0.03, 0.02, 0.02, 0.03, 0.02, 0.02, 0.02]

Depends directly on:

• Hourly data transferred for Generative AI model job in usage pattern

through the following calculations:

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

hourly_data_stored_across_usage_patterns

Hourly generative ai model job data stored across usage patterns in megabyte.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in MB:
first 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
last 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

Depends directly on:

• Hourly data stored for Generative AI model job in usage pattern

through the following calculations:

You can also visit the link to Hourly Generative AI model job data stored across usage patterns’s full calculation graph.

hourly_occurrences_across_usage_patterns

Hourly generative ai model job occurrences across usage patterns in occurrence.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in occurrence:
first 10 vals [2.0, 4.0, 4.0, 8.0, 4.0, 2.0, 3.0, 6.0, 2.0, 6.0],
last 10 vals [4.0, 3.0, 7.0, 6.0, 3.0, 5.0, 6.0, 4.0, 5.0, 4.0]

Depends directly on:

• Hourly Generative AI model job occurrences in UsagePattern usage pattern

through the following calculations:

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