[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83580-en":3,"doc-seo-83580-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83580,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","The Rising Unsustainability of AI Graphics Cards Production","Rapid advances in Artificial Intelligence have sharply increased both computational and environmental costs, fueled by large-scale investment in AI infrastructure, hardware, and software. Graphics cards now sit at the core of AI training, requiring frequent upgrades as computational demands grow. Yet the environmental damages linked to graphics card production have received limited study. This work estimates production-related impacts from 2013–2025 by analyzing trends in energy use, carbon emissions, and resource depletion, compiling a dataset for NVIDIA workstation cards.","The Rising Unsustainability of AI Graphics Cards Production  \nClément Morand  \n[clement.morand@lisn.fr](clement.morand@lisn.fr)[ ](clement.morand@lisn.fr)Université Paris-Saclay, CNRS, LISNOrsay, France  \nAurélie Névéol  \n[aurelie.neveol@lisn.fr](aurelie.neveol@lisn.fr)[ ](aurelie.neveol@lisn.fr)Université Paris-Saclay, CNRS, LISNOrsay, France  \nAnne-Laure Ligozat  \n[anne-laure.ligozat@lisn.fr](anne-laure.ligozat@lisn.fr)[ ](anne-laure.ligozat@lisn.fr)Université Paris-Saclay, CNRS, LISN, ensIIE Orsay, France  \narXiv :2607 .0 1258v 1 [ cs .CY] 5 Jun 2026  \nAbstract  \nThe rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven by large-scale investments in AI infrastructure, hardware, and software. In particular, graphics cards have become central to AI training, with frequent hardware updates required to meet escalating computational demands. However, the environmental damages of graphics cards production remain understudied.  \nThis study addresses this gap by estimating the environmental damages associated with graphics cards production over the past decade (2013–2025) . We analyze trends in energy consumption, carbon emissions and resource depletion.  \nWe compile and provide a dataset documenting the environmental damages of NVIDIA workstation graphics cards production since 2013 . Our analysis of this dataset reveals a steady increase in production-related impacts over the period.  \nOur finding highlights the need for greater transparency in lifecycle data, a persistent challenge in AI environmental assessments. While operational efficiency improvements (e.g., energy-efficient training, carbon-aware computing) are often prioritized, our results underscore that production-related impacts are also escalating and cannot be overlooked.  \nThe AI community must move beyond incremental optimizationsand confront the necessity of sufficiency. This shift may demand structural changes such as policy interventions, hardware design for longevity, and cultural shifts away from perpetual growth and increased performance.  \nKeywords  \nAI, graphics cards, embodied impacts, carbon footprint, resource depletion, environmental trends, Life Cycle Assessment, computational limits, environmental damages, sustainability  \n1 Introduction  \nThe growing carbon footprint of Information and Communication Technology (ICT) is documented [16, 30], with a relatively even distribution of greenhouse gas emissions related to user devices, networks, and datacenters. In recent years, a large hype around Artificial Intelligence (AI) with generative general purpose large language models has unfolded [53] . This has lead to massive investments and deployments of AI infrastructures, equipment and software, causing concerns around the unsustainability of such deployment in a sector already following an unsustainable trajectory. Campaigns like Save the AI [https://savethe. ai/ or](https://savethe. ai/ or) reports like the one from the Shift project [48] alert on the energy and water consumption of AI. Big  \nLIMITS ’26, Online 2026.  \ntech companies have presented AI as one of the main drivers of the growth of their carbon footprint and preventing them from attaining their (now dismissed1 ) decarbonation targets [17, 32] .  \nDifferent studies have documented evolution in datacenter energy consumption, and assessed the role the current AI hype plays [21, 31, 44, 48] . They show increases in demand that are not compensated by increases in datacenter efficiency anymore and highlight the preponderant share of the increases in energy consumption that are related to AI activities.  \nDatacenters use a large quantity of servers for storage and compute, in addition to networking equipment, cooling infrastructuresand electrical installations. Their environmental impacts thus do not only occur during usage but also during the complete life cycle of these appliances. In particular, if servers ","cbCaimrzQvpsPqCu","https://ap.wps.com/l/cbCaimrzQvpsPqCu","pdf",518028,3,1,10,"English","en",105,"# Abstract\n# 1 Introduction","[{\"question\":\"What problem does the study address about AI graphics card production?\",\"answer\":\"Environmental damages from graphics card production are understudied, leaving gaps in understanding the material unsustainability of AI deployments.\"},{\"question\":\"How does the study estimate environmental impacts from graphics card production?\",\"answer\":\"It analyzes trends in energy consumption, carbon emissions, and resource depletion over 2013–2025 and compiles a dataset for NVIDIA workstation graphics cards production since 2013.\"},{\"question\":\"What does the study conclude about production-related impacts and needed actions?\",\"answer\":\"Production-related impacts show a steady increase, indicating that focusing only on operational efficiency is insufficient. The AI community should improve lifecycle data transparency and consider structural changes such as longevity-focused hardware and policy interventions.\"}]",1784188970,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-rising-unsustainability-of-ai-graphics-cards-production","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/the-rising-unsustainability-of-ai-graphics-cards-production/83580/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-23","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address about AI graphics card production?","Question",{"text":75,"@type":76},"Environmental damages from graphics card production are understudied, leaving gaps in understanding the material unsustainability of AI deployments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study estimate environmental impacts from graphics card production?",{"text":80,"@type":76},"It analyzes trends in energy consumption, carbon emissions, and resource depletion over 2013–2025 and compiles a dataset for NVIDIA workstation graphics cards production since 2013.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the study conclude about production-related impacts and needed actions?",{"text":84,"@type":76},"Production-related impacts show a steady increase, indicating that focusing only on operational efficiency is insufficient. The AI community should improve lifecycle data transparency and consider structural changes such as longevity-focused hardware and policy interventions.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":22,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]