Environmental footprint
OpenGateLLM tracks the environmental impact of AI model usage through the EcoLogits library, which provides a comprehensive view of the environmental footprint of generative AI models at inference.
Model carbon footprint configuration
For each model provider, you can define the carbon footprint parameters. Either through the Provider settings page in the Playground UI, or via the config.yml file. For more details on model configuration, see the model addition documentation.
- By Playground UI
- By configuration file
For each model provider, you can define the carbon footprint parameters in the Provider settings page of the Playground. The form fields includes the number of total and active parameters (in billions), as well as the hosting zone.

For each model provider, you can define the carbon footprint parameters in the config.yml file. The following parameters are used for carbon footprint computation:
model_total_paramsmodel_active_paramsmodel_hosting_zone
For more information, see Configuration documentation.
Example:
models:
[...]
- name: my-language-model
type: text-generation
providers:
- type: openai
url: https://api.openai.com
key: ${OPENAI_API_KEY}
model_name: gpt-4o-mini
model_total_params: 35
model_active_params: 35
model_hosting_zone: WOR
Carbon footprint computation requires model_total_params and model_active_params to be defined. If not provided, the environmental impact will not be computed for that model provider (display as 0 kWh and 0 kgCO2eq).
Carbon footprint is only supported for text-generation and image-text-to-text model types. For other model types, the environmental impact will not be computed (display as 0 kWh and 0 kgCO2eq).
Environmental impact metrics
For each call to a generative AI model, the API returns environmental impact metrics in the response, in the usage.carbon field. The metrics include minimum and maximum estimates to account for variability in model efficiency:
- kWh: Energy consumption in kilowatt-hours (kWh), representing the final electricity consumption.
- kgCO2eq: Global Warming Potential (GWP) in kilograms of CO2 equivalent (kgCO2eq), representing greenhouse gas emissions related to climate change.
Example response:
{
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1677652288,
"model": "my-language-model",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I help you today?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 20,
"total_tokens": 30,
"cost": 0.000015,
"carbon": {
"kWh": {
"min": 0.0001234,
"max": 0.0001456
},
"kgCO2eq": {
"min": 0.0000567,
"max": 0.0000672
}
}
}
}
After the request is processed, the carbon footprint of the request is store in usage table by the hooks decorator attached to each endpoint. See usage monitoring documentation for more information.
You can also see the carbon footprint of the request in the Usage page of the Playground.

How it works
Environmental impact is calculated using the EcoLogits library through the compute_llm_impacts function. The computation takes into account:
-
Model parameters:
- Total number of parameters (
model_total_params) - Active number of parameters (
model_active_params, defaults to total params if not specified)
- Total number of parameters (
-
Token usage: The number of output/completion tokens generated by the model
-
Request latency: The time taken for the inference request (in seconds)
-
Electricity mix: The carbon intensity of electricity based on the hosting zone (
model_hosting_zone). The electricity mix includes:- ADPE (Abiotic Depletion Potential - elements)
- PE (Primary Energy)
- GWP (Global Warming Potential)
For more information about methodology, see EcoLogits documentation.
If total_params is not defined or token_count is zero, the carbon footprint will be returned as zero for both energy and emissions.