The Dirty Secret of SSDs: Embodied Carbon
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Abstract (arXiv)
Scalable Solid-State Drives (SSDs) have ushered in a transformative era in data storage and accessibility, spanning both data centers and portable devices. However, the strides made in scaling this technology can bear significant environmental consequences. On a global scale, a notable portion of semiconductor manufacturing relies on electricity derived from coal and natural gas sources. A striking example of this is the manufacturing process for a single Gigabyte of Flash memory, which emits approximately 0.16 Kg of CO2 - a considerable fraction of the total carbon emissions attributed to the system. Remarkably, the manufacturing of storage devices alone contributed to an estimated 20 million metric tonnes of CO2 emissions in the year 2021. In light of these environmental concerns, this paper delves into an analysis of the sustainability trade-offs inherent in Solid-State Drives (SSDs) when compared to traditional Hard Disk Drives (HDDs). Moreover, this study proposes methodologies to gauge the embodied carbon costs associated with storage systems effectively. The research encompasses four key strategies to enhance the sustainability of storage systems. In summation, this paper critically addresses the embodied carbon issues associated with SSDs, comparing them with HDDs, and proposes a comprehensive framework of strategies to enhance the sustainability of storage systems.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2022.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 2.
6 papers.
- 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 71
- 2024LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demandcited by 2
- 2025Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Racecited by 2
- 2025Cache Your Prompt When It's Green: Carbon-Aware Caching for Large Language Model Servingcited by 1
- 2026LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellitescited by 0
- 2026Green BOA: Determining the environmental break-even point for ML-based data compressioncited by 0
- 2026Green BOA: Determining the environmental break-even point for ML-based data compression20 Aug 2026
- 2026LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites7 May 2026
- 2025Cache Your Prompt When It's Green: Carbon-Aware Caching for Large Language Model Serving29 May 2025
- 2025Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race27 Feb 2025
- 2024LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand29 Mar 2024
- 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models25 Sep 2023