Hubblo - Graphics card environmental impact calculator

GPU embedded impacts calculator

This calculator estimates the embedded impacts (manufacturing, distribution, end-of-life) of a graphics card based on 16 Product Environmental Footprint (PEF) impact criteria (excluding the use phase). It is based on the parametric model developed for ADEME by Tide and Hubblo as part of research published in 2025 (Falk et al., 2025). The model is the result of extensive data collection, including the dismantling of a dozen graphics cards and the performance of a dozen elemental analyses.

Read the full report

The different components of a graphics card are depicted: the heatsink and the casing, and outside the casing, the card itself, with its printed wiring board, its video memory and its graphics processing unit.

Graphics card parameters

Select a graphics card:

Results

The results for a selection of impacts criteria are given for the computed graphics card impacts across its life-cycle (excluding use phase).

Depletion of fossil resources

2445 MJ
Global warming potential

181 kg CO²eq
Depletion of mineral resources

0.00690 kg SBeq

Equivalents

Equivalents in other products are given for several impact criteria for the computed graphics card impacts across its life-cycle (excluding use phase).

Depletion of fossil resources

🛢 66 liters of crude oil
Global warming potential

🚗 723 kilometers traveled by car
Depletion of mineral resources

Cu 5 kilograms of copper

NVIDIA A100 PCIe 40GB (Parametric model)

Display impact factors by:

Graphics card impact factors

This plot shows the graphics card impacts by life cycle phase (excluding use) for all impact criteria.

Show absolute values table

NVIDIA A100 PCIe 40GB impact factors per life cycle step, absolute values
Life cycle step	ADPe	ADPf	AP	CTUe	CTUh_c	CTUh_nc	Epf	Epm	Ept	GWP	GWPb	GWPf	GWPlu	IR	LU	ODP	PM	POCP	WU	MIPS	DEEE	TPE
Manufacturing
Transport
Use
End-of-life

Licence and citation

Citation BibTeX

@misc{lees-perasso2026lcagpu,
        title   = {Analyses de cycle de vie de GPU pour l’intelligence artificielle},
        author  = {Etienne Lees-Perasso, David Ekchajzer, Gauthier Roussilhe, Thomas De Latour},
        year    = {2026},
        publisher = {ADEME},
        url     = {tbd}
      }

This work is licensed under CC BY-SA 4.0

Research partners and acknowledgments

We would like to thank ADEME, CNRS and INRIA, members of the AltImpact program, for the funding and technical support that made this project possible.

We would also like to thank all the stakeholders who contributed to the research project that resulted in the publication of the article on which this work is built : Sophia Falk (University of Bonn), Thibault Pirson (ICTeam), Augustin Wattiez (ICTeam), Lisa Biber-Freudenberger (University of Bonn), Sasha Luccioni (Hugging Face), Aimee van Wynsberghe (University of Bonn), and Julien Comel (TND).

Finally, we would like to thank all the stakeholders in the ecosystem who contributed to the completion of this project. Atos, Boavizta, Bull, Scaleway, Surf, Philippe Desmaison, Aurore Vaudatin (OVH), James Martin (Shippeo), Thomas Brillant (ADEME), Benjamin Paulmier (Carbone 4), Amael Parreaux-Ey (Resilio), Vincent Schellekens (IMEC).