# Tracarbon: Track GPU power and carbon emissions while running local LLMs

> Source: <https://github.com/fvaleye/tracarbon>
> Published: 2026-09-07 14:12:10+00:00

Tracarbon is a Python library that tracks your device's energy consumption and calculates your carbon emissions.

It detects your location and your device automatically before starting to export measurements to an exporter. It could be used as a CLI with already defined metrics or programmatically with the API by defining the metrics that you want to have.

Read more in this [article](https://medium.com/@florian.valeye/tracarbon-track-your-devices-carbon-footprint-fb051fcc9009).

```
# Install Tracarbon
pip install tracarbon
# Install one or more exporters from the list
pip install 'tracarbon[datadog,prometheus,kubernetes]'
```

| **Devices** | **Description** | 
|---|---|
| Mac | ✅ Apple Silicon CPU, GPU, memory and Neural Engine energy via IOReport (no sudo). Excludes display and peripherals. Fallbacks: `powermetrics` (sudo), then`ioreg` . | 
| Linux | ✅ Supports Intel and AMD processors via [RAPL](https://web.eece.maine.edu/~vweaver/projects/rapl/) . Intel uses the powercap interface. AMD is supported on kernel 5.8+ (powercap) or via the`amd_energy` driver (HWMON). Works with containers on[Kubernetes](https://kubernetes.io/) using the[Metric API](https://kubernetes.io/docs/tasks/debug/debug-cluster/resource-metrics-pipeline/#metrics-api) if available. | 
| Windows | ✅ NVIDIA GPU power via `nvidia-smi` . CPU, memory and host totals are unavailable. | 

| **Cloud Provider** | **Description** | 
|---|---|
| AWS | ✅ Use the hardware's usage with the EC2 instances carbon emissions datasets of [cloud-carbon-coefficients](https://github.com/cloud-carbon-footprint/ccf-coefficients/blob/main/data/aws-instances.csv) . | 
| GCP | ✅ Use the hardware's usage with the GCP instances carbon emissions datasets of [cloud-carbon-coefficients](https://github.com/cloud-carbon-footprint/ccf-coefficients/blob/main/data/gcp-instances.csv) . | 
| Azure | ✅ Use the hardware's usage with the Azure instances carbon emissions datasets of [cloud-carbon-coefficients](https://github.com/cloud-carbon-footprint/ccf-coefficients/blob/main/data/azure-instances.csv) . | 

| **GPU** | **Description** | 
|---|---|
| NVIDIA | ✅ Supported via `nvidia-smi` . Works on Linux, Windows, and Intel Macs. Supports multiple GPUs. | 
| AMD | ✅ Supported via `rocm-smi` or`amd-smi` on Linux. Supports multiple GPUs. | 
| Apple Silicon | ✅ Integrated GPU power via IOReport, without sudo. Falls back to `powermetrics` (requires sudo). | 
| Intel | ❌ Not yet implemented. | 

| **Exporter** | **Description** | 
|---|---|
| Stdout | Print the metrics in Stdout. | 
| JSON | Write the metrics in a JSON file. | 
| Prometheus | Send the metrics to Prometheus. | 
| Datadog | Send the metrics to Datadog. | 

| **Location** | **Description** | **Source** | 
|---|---|---|
| Worldwide | Get the latest co2g/kwh in near real-time using the CO2Signal or ElectricityMaps APIs. See [here](https://app.electricitymaps.com/developer-hub/api/reference) for available Electricity Maps query modes. | [CO2Signal API](https://www.co2signal.com) or[ElectricityMaps](https://app.electricitymaps.com/developer-hub/api/reference) | 
| Europe | Static file created from the European Environment Agency Emission for the co2g/kwh in European countries. | [EEA website](https://www.eea.europa.eu/en/analysis/maps-and-charts/co2-emission-intensity-15) | 
| AWS | Static file of the AWS Grid emissions factors. | [cloud-carbon-coefficients](https://github.com/cloud-carbon-footprint/cloud-carbon-coefficients/blob/main/data/grid-emissions-factors-aws.csv) | 
| GCP | Static file of the GCP Grid emissions factors (2024 yearly data). | [GoogleCloudPlatform/region-carbon-info](https://github.com/GoogleCloudPlatform/region-carbon-info/blob/main/data/yearly/2024.csv) | 
| Azure | Static file of the Azure Grid emissions factors. | [cloud-carbon-coefficients](https://github.com/cloud-carbon-footprint/cloud-carbon-coefficients/blob/main/data/grid-emissions-factors-azure.csv) | 

The environment variables can be set from an environment file `.env`.

| **Parameter** | **Description** | 
|---|---|
| TRACARBON_CO2SIGNAL_API_KEY | The api key received from [CO2Signal](https://www.co2signal.com) or[ElectricityMaps](https://app.electricitymaps.com/developer-hub/api/reference) . | 
| TRACARBON_CO2SIGNAL_URL | The url of [CO2Signal](https://docs.co2signal.com/#get-latest-by-country-code) is the default endpoint to retrieve the last known state of the zone, but it could be changed to[ElectricityMaps](https://app.electricitymaps.com/developer-hub/api/reference) . | 
| TRACARBON_METRIC_PREFIX_NAME | The prefix to use in all the metrics name. | 
| TRACARBON_INTERVAL_IN_SECONDS | The interval in seconds to wait between the metrics evaluation. | 
| TRACARBON_LOG_LEVEL | The level to use for displaying the logs. | 
| TRACARBON_IPINFO_TOKEN | An optional [ipinfo.io](https://ipinfo.io) API token used for country detection from the IP address, lifting the anonymous rate limit. | 
| TRACARBON_KUBERNETES_NODE_NAME | The Kubernetes node name used to scope container metrics to the node being measured. Falls back to `NODE_NAME` when unset. | 

**Request your API key**

- Go to [CO2Signal](https://www.co2signal.com/) and get your free API key for non-commercial use, or go to[ElectricityMaps](https://app.electricitymaps.com/developer-hub/api/reference) for commercial use.
- This API is used to retrieve the last known carbon intensity (in gCO2eq/kWh) of electricity consumed in your location.
- Set your API key in the environment variables, in the `.env` file or directly in the configuration.
- If you would like to start without an API key, it's possible, the carbon intensity will be loaded statistically from a file.
- Launch Tracarbon 🚀

**Command Line**

```
tracarbon run
```

**Prometheus with Kubernetes containers**

```
tracarbon run --exporter-name Prometheus --containers
```

With the default metric prefix, container metrics are exposed with these Prometheus names:

| **Metric** | **Labels** | 
|---|---|
| tracarbon_energy_consumption_kubernetes_total | pod_name, pod_namespace, container_name, platform, containers, location, units | 
| tracarbon_energy_consumption_kubernetes_cpu | pod_name, pod_namespace, container_name, platform, containers, location, units | 
| tracarbon_energy_consumption_kubernetes_memory | pod_name, pod_namespace, container_name, platform, containers, location, units | 
| tracarbon_carbon_emission_kubernetes_total | pod_name, pod_namespace, container_name, platform, containers, location, source, units | 
| tracarbon_carbon_emission_kubernetes_cpu | pod_name, pod_namespace, container_name, platform, containers, location, source, units | 
| tracarbon_carbon_emission_kubernetes_memory | pod_name, pod_namespace, container_name, platform, containers, location, source, units | 

Zero values are exported. If Kubernetes returns no pod metrics, the CLI logs `No Kubernetes container metrics were collected.` Host metrics are still exported.

When running in Kubernetes, deploy Tracarbon per node and set `NODE_NAME` from `spec.nodeName` with the Downward API so container metrics are scoped to the measured node.

**API**

``` python
from tracarbon import TracarbonBuilder, TracarbonConfiguration

configuration = TracarbonConfiguration() # Your configuration
tracarbon = TracarbonBuilder(configuration=configuration).build()
tracarbon.start()
# Your code
total_co2g = tracarbon.stop() # The CO2 grams emitted while it was running

with tracarbon:
    # Your code

report = tracarbon.report # Get the report
print(report.total_co2g)
```

`total_co2g` is `None` when no host carbon emission metric was collected. The total reflects collected samples.

**Local: using uv**

```
make init
make test-unit
```

The documentation is hosted here: [https://fvaleye.github.io/tracarbon/documentation](https://fvaleye.github.io/tracarbon/documentation)

- 2025-11 [Carbon Emission Quantification of Machine Learning: A Review](https://doi.org/10.1109/TSUSC.2025.3578834)
- 2025-04-25 [A Critical Analysis of Machine Learning Eco-feedback Tools through the Lens of Sustainable HCI](https://doi.org/10.1145/3706598.3713198)
- 2025-04-25 ["Should I choose a smaller model?": Understanding ML Model Selection and Its Impact on Sustainability](https://doi.org/10.1145/3706598.3713240)
- 2024-07-08 [Balancing computational chemistry's potential with its environmental impact](https://doi.org/10.1039/D4GC01745E)
- 2023-06-26 [GREENER principles for environmentally sustainable computational science](https://doi.org/10.1038/s43588-023-00461-y)
- 2023-01-19 [eco2AI: Carbon Emissions Tracking of Machine Learning Models as the First Step Towards Sustainable AI](https://doi.org/10.1134/S1064562422060230)