# Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trends

arXiv: https://arxiv.org/abs/2502.01671 · HTML: https://mlsanity.com/paper/2502.01671/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 56 references that are in the map.
- First posted on arXiv 1 Feb 2025.

## Builds on (most cited first, 13 of 13)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings](https://mlsanity.com/paper/2304.01433/) (2023)
- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [Measuring the Algorithmic Efficiency of Neural Networks](https://mlsanity.com/paper/2005.04305/) (2020)
- [Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning](https://mlsanity.com/paper/2302.08476/) (2023)
- [How to estimate carbon footprint when training deep learning models? A guide and review](https://mlsanity.com/paper/2306.08323/) (2023)
- [Carbon accounting in the Cloud: a methodology for allocating emissions across data center users](https://mlsanity.com/paper/2406.09645/) (2024)

## Built on by (most cited first, 10 of 10)

- [Measuring the environmental impact of delivering AI at Google Scale](https://mlsanity.com/paper/2508.15734/) (2025)
- [The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound](https://mlsanity.com/paper/2510.09022/) (2025)
- [Improving AI Efficiency in Data Centres by Power Dynamic Response](https://mlsanity.com/paper/2510.11119/) (2025)
- [AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability](https://mlsanity.com/paper/2601.19886/) (2026)
- [The data heat island effect: quantifying the impact of AI data centers in a warming world](https://mlsanity.com/paper/2603.20897/) (2026)
- [Environmental Footprint of GenAI Research: Insights from the Moshi Foundation Model](https://mlsanity.com/paper/2604.11154/) (2026)
- [Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment](https://mlsanity.com/paper/2606.07632/) (2026)
- [Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing So](https://mlsanity.com/paper/2606.18144/) (2026)
- [Lights, Camera, Carbon: Architectural Scaling Laws for Video Generation Energy Consumption](https://mlsanity.com/paper/2607.04553/) (2026)
- [Auto-Scaling Heterogeneous Neural Processing Units for Energy and Cost-Efficient LLM Serving](https://mlsanity.com/paper/2607.16488/) (2026)

## Abstract (arXiv)

Specialized hardware accelerators aid the rapid advancement of artificial intelligence (AI), and their efficiency impacts AI's environmental sustainability. This study presents the first publication of a comprehensive AI accelerator life-cycle assessment (LCA) of greenhouse gas emissions, including the first publication of manufacturing emissions of an AI accelerator. Our analysis of five Tensor Processing Units (TPUs) encompasses all stages of the hardware lifespan - from raw material extraction, manufacturing, and disposal, to energy consumption during development, deployment, and serving of AI models. Using first-party data, it offers the most comprehensive evaluation to date of AI hardware's environmental impact. We include detailed descriptions of our LCA to act as a tutorial, road map, and inspiration for other computer engineers to perform similar LCAs to help us all understand the environmental impacts of our chips and of AI. A byproduct of this study is the new metric compute carbon intensity (CCI) that is helpful in evaluating AI hardware sustainability and in estimating the carbon footprint of training and inference. This study shows that CCI improves 3x from TPU v4i to TPU v6e. Moreover, while this paper's focus is on hardware, software advancements leverage and amplify these gains.