GenAIJob
Params
name
A human readable description of the object.
service
An instance of GenAIModel.
output_token_count
generative ai model job output token count from e-footprint hypothesis in dimensionless.
Backwards links
Calculated attributes
output_token_weights
ExplainableQuantity in kilobyte, representing the generative ai model job output token weights.
Example value: 3.0 kilobyte
Depends directly on:
- Generative AI model job output token count from e-footprint hypothesis
- Number of bits per token from e-footprint hypothesis
through the following calculations:
You can also visit the link to Generative AI model job output token weights’s full calculation graph.
data_stored
ExplainableQuantity in kilobyte, representing the generative ai model job data stored.
Example value: 103.0 kilobyte
Depends directly on:
through the following calculations:
You can also visit the link to Generative AI model job data stored’s full calculation graph.
data_transferred
ExplainableQuantity in kilobyte, representing the generative ai model job data transferred.
Example value: 103.0 kilobyte
Depends directly on:
through the following calculations:
You can also visit the link to Generative AI model job data transferred’s full calculation graph.
request_duration
ExplainableQuantity in nanosecond, representing the generative ai model job request duration.
Example value: 28154600000.0 nanosecond
Depends directly on:
- Generative AI model job output token count from e-footprint hypothesis
- GPU latency per active parameter and output token from Ecologits
- open-mistral-7b from mistralai nb of active parameters from Ecologits
- Base GPU latency per output_token from Ecologits
through the following calculations:
You can also visit the link to Generative AI model job request duration’s full calculation graph.
ram_needed
ExplainableQuantity in gigabyte, representing the no additional gpu ram needed because model is already loaded in memory from e-footprint hypothesis.
Example value: 0.0 gigabyte
Depends directly on:
through the following calculations:
You can also visit the link to No additional GPU RAM needed because model is already loaded in memory from e-footprint hypothesis’s full calculation graph.
compute_needed
ExplainableQuantity in gpu, representing the generative ai model job nb of required gpus during inference.
Example value: 0.22 gpu
Depends directly on:
- Generative AI model ratio between GPU memory footprint and model size from Ecologits
- open-mistral-7b from mistralai nb of active parameters from Ecologits
- Generative AI model nb of bits per parameter from e-footprint hypothesis
- on premise GPU server RAM per GPU from Estimating the Carbon Footprint of BLOOM
through the following calculations:
You can also visit the link to Generative AI model job nb of required GPUs during inference’s full calculation graph.
hourly_occurrences_per_usage_pattern
Dictionary with UsagePattern as keys and hourly generative ai model job occurrences in usage pattern as values, in dimensionless.
Example value: {
id-46655e-usage-pattern: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in dimensionless:
first 10 vals [5.0, 9.0, 1.0, 4.0, 4.0, 8.0, 4.0, 6.0, 5.0, 5.0],
last 10 vals [8.0, 6.0, 9.0, 3.0, 9.0, 1.0, 5.0, 1.0, 9.0, 1.0],
}
Depends directly on:
through the following calculations:
You can also visit the link to Hourly Generative AI model job occurrences in 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 dimensionless.
Example value: {
id-46655e-usage-pattern: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in dimensionless:
first 10 vals [0.04, 0.07, 0.01, 0.03, 0.03, 0.06, 0.03, 0.05, 0.04, 0.04],
last 10 vals [0.06, 0.05, 0.07, 0.02, 0.07, 0.01, 0.04, 0.01, 0.07, 0.01],
}
Depends directly on:
- Hourly Generative AI model job occurrences in usage pattern
- Generative AI model job request duration
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 kilobyte.
Example value: {
id-46655e-usage-pattern: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in kB:
first 10 vals [515.0, 927.0, 103.0, 412.0, 412.0, 824.0, 412.0, 618.0, 515.0, 515.0],
last 10 vals [824.0, 618.0, 927.0, 309.0, 927.0, 103.0, 515.0, 103.0, 927.0, 103.0],
}
Depends directly on:
- Hourly Generative AI model job occurrences in usage pattern
- Generative AI model job data transferred
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 kilobyte.
Example value: {
id-46655e-usage-pattern: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in kB:
first 10 vals [515.0, 927.0, 103.0, 412.0, 412.0, 824.0, 412.0, 618.0, 515.0, 515.0],
last 10 vals [824.0, 618.0, 927.0, 309.0, 927.0, 103.0, 515.0, 103.0, 927.0, 103.0],
}
Depends directly on:
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_occurrences_across_usage_patterns
hourly generative ai model job occurrences across usage patterns in dimensionless.
Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in dimensionless:
first 10 vals [5.0, 9.0, 1.0, 4.0, 4.0, 8.0, 4.0, 6.0, 5.0, 5.0],
last 10 vals [8.0, 6.0, 9.0, 3.0, 9.0, 1.0, 5.0, 1.0, 9.0, 1.0]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly Generative AI model job occurrences across usage patterns’s full calculation graph.
hourly_avg_occurrences_across_usage_patterns
hourly generative ai model job average occurrences across usage patterns in dimensionless.
Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in dimensionless:
first 10 vals [0.04, 0.07, 0.01, 0.03, 0.03, 0.06, 0.03, 0.05, 0.04, 0.04],
last 10 vals [0.06, 0.05, 0.07, 0.02, 0.07, 0.01, 0.04, 0.01, 0.07, 0.01]
Depends directly on:
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 kilobyte.
Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in kB:
first 10 vals [515.0, 927.0, 103.0, 412.0, 412.0, 824.0, 412.0, 618.0, 515.0, 515.0],
last 10 vals [824.0, 618.0, 927.0, 309.0, 927.0, 103.0, 515.0, 103.0, 927.0, 103.0]
Depends directly on:
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 kilobyte.
Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in kB:
first 10 vals [515.0, 927.0, 103.0, 412.0, 412.0, 824.0, 412.0, 618.0, 515.0, 515.0],
last 10 vals [824.0, 618.0, 927.0, 309.0, 927.0, 103.0, 515.0, 103.0, 927.0, 103.0]
Depends directly on:
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.