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Text of the page (random words):
d cloud infrastructure as the first two layers of a supply chain that continues through data foundation models and applications 3 under these definitions data algorithms trained models and applications depend on infrastructure but are not necessarily treated as infrastructure themselves an ai data center is a facility level component of ai infrastructure rather than a synonym for the entire stack infrastructure may support both ai and non ai workloads general purpose computing and accelerated computing share facilities storage and some network equipment while differing in processors interconnect requirements power density and cost 6 components edit computing hardware edit ai systems run on central processing units cpus and on specialised accelerators that perform large numbers of operations in parallel common accelerators include graphics processing units gpus application specific integrated circuits asics and field programmable gate arrays fpgas different workloads use different combinations of processors large scale model training commonly relies on accelerators while some inference and supporting tasks can run on general purpose cpus 2 6 accelerators are assembled into servers and then into clusters their usefulness depends on associated memory storage networking and systems software so processor counts alone do not describe usable ai capacity 2 the semiconductor supply chain behind these systems includes chip design manufacturing equipment fabrication packaging and materials it is globally distributed but contains geographically concentrated inputs and specialised production stages creating dependencies and potential bottlenecks 7 memory storage and networking edit training and operating ai models requires moving data between processors memory and storage servers use memory close to the processors for active calculations while storage systems retain training data intermediate results and deployed model files high bandwidth connections link processors within a server servers within a cluster and clusters to storage and external networks 2 large training workloads are often distributed across many machines distribution can reduce the time needed for a computation but it introduces coordination and communication problems including how to parallelise training efficiently and maintain a coherent model across workers 8 consequently cluster performance depends not only on arithmetic capacity but also on memory bandwidth network latency network bandwidth and the way work is divided data centres and cloud services edit data centres provide the buildings racks servers storage network equipment power distribution backup systems and cooling needed to operate computing hardware some organisations build private clusters while others rent virtual machines accelerator instances or managed ai services from public cloud providers cloud regions are physical hubs that place computing capacity in particular geographic and legal jurisdictions 2 9 cloud platforms combine infrastructure with model development tools databases security services and application marketplaces this integration can make large scale resources available without building a private data centre but it can also create technical and commercial dependence on a provider s interfaces services and software ecosystem 6 10 software edit the software layer translates model operations into work that processors can execute it includes device drivers compilers programming libraries and machine learning frameworks as well as software for assigning accelerators scheduling jobs and monitoring clusters deployed systems also need data pipelines model serving systems and access controls 2 11 production machine learning systems must handle requirements such as large and heterogeneous data flows low latency processing resource management security and privacy software architecture therefore affects how efficiently physical capacity can be used and how reliably a model can be integrated into a larger application 11 electricity and cooling edit ai computing equipment requires electrical supply power conversion and distribution uninterruptible power supplies or other backup systems and cooling these supporting systems take up space and consume energy in addition to the processors themselves accelerated clusters can require more power and cooling than general purpose servers and rapid changes in workload can create large variations in electrical load 12 5 the siting of infrastructure is therefore affected by access to grid connections reliable electricity cooling technology water telecommunications links and suitable land these constraints can determine whether announced projects are built and how quickly capacity becomes available 2 5 training and inference edit machine learning training uses data to adjust a model s parameters advanced training runs may occupy large accelerator clusters for extended periods and use distributed software to divide calculations and exchange intermediate results a study of historical machine learning systems estimated that training compute doubled about every six months after the emergence of deep learning in the early 2010s although trends vary by model class and time period 13 inference applies a trained model to new inputs its infrastructure requirements range from small latency sensitive deployments to high volume services running large models in data centres some inference workloads use accelerators while smaller or more efficient models may run on general purpose processors cost response time throughput and availability influence the choice 6 software and hardware efficiency can reduce the resources needed for a given level of performance between november 2022 and october 2024 the inference cost of a system performing at the level of gpt 3 5 fell more than 280 fold 14 lower unit costs do not necessarily reduce total infrastructure demand if the number scale or complexity of uses grows 5 economics and access edit scale and market structure edit ai infrastructure has high fixed costs at several layers advanced semiconductor production requires specialised facilities and equipment while cloud and data centre operators invest in land buildings power systems networking storage and large numbers of processors scale can lower unit costs and support a wider range of services but it can also make entry difficult for smaller firms 7 3 6 large technology companies operate across several layers of the ai supply chain including cloud infrastructure model development and applications vertical integration can simplify deployment and fund large investments while also raising competition concerns about access switching preferential treatment and dependence on a small number of providers 1 10 the uk competition and markets authority found that accelerated cloud computing required significant investment in accelerator chips and specialised infrastructure and that large sunk costs and economies of scale were barriers in cloud services 6 geographic distribution and public capacity edit ai computing capacity is distributed unevenly public cloud capacity is organised into regions so users practical access can depend on whether specialised hardware is available domestically or in a nearby jurisdiction location also affects latency data governance obligations resilience and exposure to trade or supply restrictions 9 governments have consequently considered public shared or nationally supported computing capacity the oecd has described national compute planning in terms of capacity and utilisation effective access and skills and resilience issues including security sovereignty and sustainability 4 such programmes may provide access to researchers public institutions and smaller firms that cannot procure large clusters directly but they still depend on global semiconductor networking and energy supply chains 7 environmental impacts edit the environmental effects of ai infrastructure occur across the equipment life cycle they include energy and water used in semiconductor production and data centre operation greenhouse gas emissions associated with electricity generation materials used in servers and supporting equipment and electronic waste at end of life environmental assessment can distinguish these direct effects from the indirect environmental costs or benefits of applications that use ai 15 ai specific electricity use is difficult to isolate because data centres host mixed workloads and operators disclose limited comparable data the international energy agency estimated that global data centre electricity consumption was about 485 terawatt hours in 2025 and projected roughly 950 terawatt hours in 2030 in its central projection electricity use by ai focused data centres grows faster and triples over that period 5 these figures cover scenarios rather than a fixed outcome and depend on efficiency demand grid connections and the rate at which proposed facilities are completed water and emissions also vary by location electricity mix and cooling system a 2025 scenario study of ai servers in the united states estimated an annual water footprint of 731 1 125 million cubic metres and additional annual emissions of 24 44 million tonnes of carbon dioxide equivalent between 2024 and 2030 depending on expansion the authors emphasised deep uncertainty from server locations efficiency measures and grid decarbonisation 16 proposed responses include more efficient hardware and software higher equipment utilisation lower carbon electricity water aware siting and cooling greater disclosure and life cycle measurement standards 15 12 see also edit ai data center cloud computing distributed computing high performance computing mlops sovereign ai supercomputer references edit 1 2 3 4 oecd 14 november 2025 competition in artificial intelligence infrastructure report oecd roundtables on competition policy papers paris oecd publishing doi 10 1787 623d1874 en retrieved 11 july 2026 1 2 3 4 5 6 7 8 gupta aadya ranjan adarsh 30 april 2024 a primer on compute carnegie endowment for international peace retrieved 11 july 2026 1 2 3 gambacorta leonardo shreeti vatsala march 2025 the ai supply chain pdf report bis papers bank for international settlements isbn 978 92 9259 843 3 retrieved 11 july 2026 1 2 oecd 28 february 2023 a blueprint for building national compute capacity for artificial intelligence report oecd digital economy papers paris oecd publishing doi 10 1787 876367e3 en retrieved 11 july 2026 1 2 3 4 5 international energy agency 16 april 2026 key questions on energy and ai report paris international energy agency retrieved 11 july 2026 1 2 3 4 5 6 competition and markets authority 31 july 2025 cloud infrastructure services final decision report pdf report competition and markets authority retrieved 11 july 2026 1 2 3 oecd 24 june 2025 mapping the semiconductor value chain working towards identifying dependencies and vulnerabilities report oecd science technology and industry policy papers paris oecd publishing doi 10 1787 4154cdbf en retrieved 11 july 2026 verbraeken joost wolting matthijs katzy jonathan kloppenburg jeroen verbelen tim rellermeyer jan s 2020 a survey on distributed machine learning acm computing surveys 53 2 article 3377454 arxiv 1912 09789 doi 10 1145 3377454 retrieved 11 july 2026 1 2 lehdonvirta vili wu boxi hawkins zoe jay caira celine russo lucia 29 october 2025 measuring domestic public cloud compute availability for artificial intelligence report oecd artificial intelligence papers paris oecd publishing doi 10 1787 8602a322 en retrieved 11 july 2026 1 2 van der vlist fernando helmond anne ferrari fabian 12 march 2024 big ai cloud infrastructure dependence and the industrialisation of artificial intelligence big data society 11 1 doi 10 1177 20539517241232630 retrieved 11 july 2026 1 2 cabrera christian paleyes andrei thodoroff pierre lawrence neil d 20 november 2025 machine learning systems a survey from a data oriented perspective acm computing surveys 58 5 doi 10 1145 3769292 retrieved 11 july 2026 1 2 international energy agency 10 april 2025 energy and ai report paris international energy agency retrieved 11 july 2026 sevilla jaime heim lennart ho anson besiroglu tamay hobbhahn marius villalobos pablo 2022 compute trends across three eras of machine learning 2022 international joint conference on neural networks pp 1 8 doi 10 1109 ijcnn55064 2022 9891914 retrieved 11 july 2026 stanford institute for human centered artificial intelligence 2025 the 2025 ai index report report stanford university retrieved 11 july 2026 1 2 oecd 15 november 2022 measuring the environmental impacts of artificial intelligence compute and applications the ai footprint report oecd digital economy papers paris oecd publishing doi 10 1787 7babf571 en retrieved 11 july 2026 xiao tianqi nerini francesco fuso matthews h damon tavoni massimo you fengqi 10 november 2025 environmental impact and net zero pathways for sustainable artificial intelligence servers in the usa nature sustainability 8 1541 1553 doi 10 1038 s41893 025 01681 y hdl 11311 1309509 retrieved 11 july 2026 v t e artificial intelligence ai history timeline glossary lists algorithms companies institutions projects software open source proprietary concepts automated reasoning automated planning constraint satisfaction knowledge representation parameter hyperparameter loss functions regression bias variance tradeoff double descent overfitting clustering gradient descent sgd quasi newton method conjugate gradient method backpropagation attention convolution normalization batchnorm activation softmax sigmoid rectifier gating weight initialization regularization datasets augmentation prompt engineering reinforcement learning q learning sarsa imitation policy gradient diffusion latent diffusion model autoregression adversary rag uncanny valley llm post training rlhf self supervised learning reflection recursive self improvement hallucination word embedding vibe coding blended ai open source ai sovereign ai symbolic ai neuro symbolic ai situated approach actor critic algorithm applications automated theorem proving general game playing machine learning in context learning artificial neural network deep learning language model large nmt reasoning model context protocol intelligent agent ai agent artificial human companion humanity s last exam lethal autonomous weapons laws generative ai weak ai hypothetical artificial general intelligence agi artificial superintelligence asi agent2agent protocol physical ai implementations audio visual alexnet wavenet human image synthesis hwr ocr computer vision speech synthesis 15 ai elevenlabs speech recognition whisper facial recognition alphafold text to image models aurora dall e firefly flux gpt image ideogram imagen midjourney recraft stable diffusion text to video models dream machine runway gen hailuo ai kling sora seedance veo music generation riffusion suno udio world models genie oasis ...