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{{Short description|American semiconductor company}}{{Infobox company▼
| name = Cerebras Systems Inc.▼
▲{{Short description|American semiconductor company}}
▲| name = Cerebras Systems
| logo = Cerebras logo.svg
| type = [[Privately held company|Private]]
| image = 1237 E. Arques Avenue.jpg
| image_caption = Headquarters in Sunnyvale
| genre =
| fate =
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| location_city = [[Sunnyvale, California]]
| location_country =
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| production =
| services =
| num_employees = 335 (2023)<ref>{{cite web |last=Takahashi |first=Dean |date=20 July 2023 |title=Cerebras unveils world's largest AI training supercomputer with 54M cores |url=https://venturebeat.com/ai/cerebras-unveils-worlds-larges-ai-training-supercomputer-with-54m-cores/ |website=VentureBeat}}</ref>
| homepage = {{URL|
| footnotes =
| intl =
}}
'''Cerebras Systems Inc.''' is an American [[artificial intelligence]] (AI) company with offices in [[Sunnyvale, California|Sunnyvale]]
== History ==
Cerebras was founded in 2015 by Andrew Feldman, Gary Lauterbach, Michael James, Sean Lie and Jean-Philippe Fricker.<ref name=":2">{{Cite web|last=Tilley|first=Aaron|title=AI Chip Boom: This Stealthy AI Hardware Startup Is Worth Almost A Billion|url=https://www.forbes.com/sites/aarontilley/2017/08/31/ai-chip-cerebras-systems-investment/|access-date=2021-04-30|website=Forbes|language=en}}</ref> These five founders worked together at [[SeaMicro]], which was started in 2007 by Feldman and Lauterbach and was later sold to AMD in 2012 for $334 million.<ref>{{Cite web|last=Hardy|first=Quentin|date=2012-02-29|title=A.M.D. Buying SeaMicro for $334 Million|url=https://bits.blogs.nytimes.com/2012/02/29/a-m-d-buying-seamicro-for-334-million/|access-date=2021-04-30|website=Bits Blog|language=en-US}}</ref><ref>{{Cite magazine|title=How Google Spawned The 384-Chip Server|language=en-US|magazine=Wired|url=https://www.wired.com/2012/01/seamicro-and-google/|access-date=2021-04-30|issn=1059-1028}}</ref>
In May 2016, Cerebras secured $27 million in [[series A funding]] led by [[Benchmark (venture capital firm)|Benchmark]], [[Foundation Capital]] and Eclipse Ventures
In December 2016, [[series B funding]] was led by [[Coatue Management|Coatue]], followed in January 2017 with series C funding led by VY Capital.<ref name=":2" />
In 2020, the company announced an office in Japan and partnership with [[Tokyo Electron|Tokyo Electron Devices]].<ref>{{Cite web| title= Cerebras Systems Expands Global Footprint with New Offices in Tokyo, Japan, and Toronto, Canada|url=https://www.yahoo.com/now/cerebras-systems-expands-global-footprint-130000796.html|access-date= August 13, 2021 |work= Press Release |author= Cerebras Systems |language= en-US}}</ref>▼
In November 2018, Cerebras closed its series D round with $88 million, making the company a [[Unicorn (finance)|unicorn]]. Investors in this round included [[Altimeter Capital|Altimeter]], VY Capital, Coatue, Foundation Capital, Benchmark, and Eclipse.<ref>{{Cite web|last=Martin|first=Dylan|date=2019-11-27|title=AI Chip Startup Cerebras Reveals 'World's Fastest AI Supercomputer'|url=https://www.crn.com/news/components-peripherals/ai-chip-startup-cerebras-systems-raises-88-million-series-d-round|access-date=2021-04-30|website=CRN}}</ref><ref name=":3">{{Cite web|last=Strategy|first=Moor Insights and|title=Cerebras Unveils AI Supercomputer-On-A-Chip|url=https://www.forbes.com/sites/moorinsights/2019/08/19/cerebras-unveils-ai-supercomputer-on-a-chip/|access-date=2021-04-30|website=Forbes|language=en}}</ref>
In April 2021, Cerebras announced the CS-2 based on the company's Wafer Scale Engine Two (WSE-2), which has 850,000 cores.<ref name=":0" /> In August 2021, the company announced its brain-scale technology that can run a [[neural network]] with over 120 trillion connections.<ref name=":11">{{Cite web|date=2021-08-24|title=Cerebras' Tech Trains "Brain-Scale" AIs|url=https://spectrum.ieee.org/cerebras-ai-computers|access-date=2021-09-22|website=IEEE Spectrum|language=en}}</ref>▼
In November 2019, Cerebras closed its series E round with over $270 million for a valuation of $2.4 billion.<ref>{{Cite news|title=Cerebras Crams More Compute Into Second-Gen 'Dinner Plate Sized' Chip|work=EE Times|url=https://www.eetimes.com/cerebras-crams-more-compute-into-second-gen-dinner-plate-sized-chip/|access-date=2021-05-12}}</ref>
In August 2022, Cerebras announced the opening of a new office in Bangalore, India.<ref name=":18" /><ref name=":19" />▼
▲In 2020, the company announced an office in Japan and partnership with [[Tokyo Electron|Tokyo Electron Devices]].<ref>{{Cite web| title= Cerebras Systems Expands Global Footprint with New Offices in Tokyo, Japan, and Toronto, Canada|url=https://www.yahoo.com/now/cerebras-systems-expands-global-footprint-130000796.html|access-date= August 13, 2021 |work= Press Release |author= Cerebras Systems |language= en-US}}</ref>
=== Funding ===▼
▲In April 2021, Cerebras announced the CS-2 based on the company's Wafer Scale Engine Two (WSE-2), which has 850,000 cores.<ref name=":0" /> In August 2021, the company announced its brain-scale technology that can run a [[neural network]] with over 120 trillion connections.<ref name=":11">{{Cite web|date=2021-08-24|title=Cerebras' Tech Trains "Brain-Scale" AIs|url=https://spectrum.ieee.org/cerebras-ai-computers|access-date=2021-09-22|website=IEEE Spectrum|language=en}}</ref>
▲Cerebras secured $27 million in [[series A funding]] led by [[Benchmark (venture capital firm)|Benchmark]], [[Foundation Capital]] and Eclipse Ventures in May 2016.<ref>{{Cite web|title=A stealthy startup called Cerebras raised around $25 million to build deep learning hardware|url=https://social.techcrunch.com/2016/12/19/a-stealthy-startup-called-cerebras-raised-around-25-million-to-build-deep-learning-hardware/|access-date=2021-04-30|website=TechCrunch|date=19 December 2016 |language=en-US}}</ref><ref name=":2" /> In December 2016, [[series B funding]] was led by [[Coatue Management]], followed in January 2017 with series C funding led by VY Capital.<ref name=":2"/>
In August 2022, Cerebras was honored by the [[Computer History Museum]] in [[Mountain View, California]]. The museum added to its permanent collection and unveiled a new display featuring the WSE-2—the biggest computer chip made so far—marking an "epochal" achievement in the history of fabricating transistors as an integrated part.<ref>{{Cite web |title=AI startup Cerebras celebrated for chip triumph where others tried and failed |url=https://www.zdnet.com/article/ai-startup-cerebras-celebrated-for-chip-triumph-where-others-tried-and-failed/ |access-date=2022-08-04 |website=ZDNet |language=en}}</ref><ref>{{Cite web |date=2022-08-03 |title=The Biggest Chip In the World |url=https://computerhistory.org/blog/the-biggest-chip-in-the-world/ |access-date=2022-08-04 |website=CHM |language=en}}</ref>
▲In August 2022, Cerebras announced the opening of a new office in Bangalore, India.<ref name=":18" /><ref name=":19" />
== Technology ==
The Cerebras Wafer Scale Engine (WSE) is a single,
The WSE-1 has 1.2 trillion transistors, 400,000 compute cores and 18 gigabytes of memory.<ref name=":4" /><ref name=":5" /><ref name=":6" />
In April 2021, Cerebras announced the CS-2 AI system based on the 2nd-generation Wafer Scale Engine (WSE-2), manufactured by the [[7 nm process]] of [[TSMC]] .<ref name=":0" /> It is 26 inches tall and fits in one-third of a standard data center rack.<ref name=":8">{{Cite web|last=Ray|first=Tiernan|date=April 20, 2021|title=Cerebras continues 'absolute domination' of high-end compute, it says, with world's hugest chip two-dot-oh|url=https://www.zdnet.com/article/cerebras-continues-absolute-domination-of-high-end-compute-it-says-with-worlds-hugest-chip-two-dot-oh/|access-date=August 13, 2021|website=ZDNet|language=en}}</ref><ref name=":0" />
The Cerebras WSE-2 has 850,000 cores and 2.6 trillion transistors.<ref name=":8" /><ref>{{Cite magazine|last=Knight|first=Will|date=August 24, 2021|title=A New Chip Cluster Will Make Massive AI Models Possible|language=en-US|magazine=Wired|url=https://www.wired.com/story/cerebras-chip-cluster-neural-networks-ai/|access-date=2021-08-25|issn=1059-1028}}</ref>
In August 2021, the company announced a system which connects multiple [[integrated circuit]]s (commonly called "chips") into a [[neural network]] with many connections.
▲In April 2021, Cerebras announced the CS-2 AI system based on the 2nd-generation Wafer Scale Engine (WSE-2), manufactured by the [[7 nm process]] of Taiwan Semiconductor Manufacturing Company ([[TSMC]]).<ref name=":0"/> It is 26 inches tall and fits in one-third of a standard data center rack.<ref name=":8">{{Cite web |last=Ray |first=Tiernan |date=April 20, 2021 |title=Cerebras continues 'absolute domination' of high-end compute, it says, with world's hugest chip two-dot-oh |url=https://www.zdnet.com/article/cerebras-continues-absolute-domination-of-high-end-compute-it-says-with-worlds-hugest-chip-two-dot-oh/ |access-date=August 13, 2021 |website=ZDNet |language=en}}</ref><ref name=":0"/> The WSE-2 has 850,000 cores and 2.6 trillion transistors.<ref name=":8"/><ref>{{Cite magazine|last=Knight|first=Will|date=August 24, 2021|title=A New Chip Cluster Will Make Massive AI Models Possible|language=en-US|magazine=Wired|url=https://www.wired.com/story/cerebras-chip-cluster-neural-networks-ai/|access-date=2021-08-25|issn=1059-1028}}</ref> The WSE-2 expanded on-chip [[static random-access memory]] (SRAM) to 40 gigabytes, memory bandwidth to 20 petabytes per second and total fabric bandwidth to 220 petabits per second.<ref>{{Cite news|title=Cerebras Systems Smashes the 2.5 Trillion Transistor Mark with New Second Generation Wafer Scale Engine|work=Bloomberg|url=https://www.bloomberg.com/press-releases/2021-04-20/cerebras-systems-smashes-the-2-5-trillion-transistor-mark-with-new-second-generation-wafer-scale-engine|access-date=2021-06-02}}</ref><ref>{{Cite web |last=Cutress |first=Ian |title=Cerebras Unveils Wafer Scale Engine Two (WSE2): 2.6 Trillion Transistors, 100% Yield |url=https://www.anandtech.com/show/16626/cerebras-unveils-wafer-scale-engine-two-wse2-26-trillion-transistors-100-yield|access-date=2021-06-03 |website=Anandtech.com}}</ref>
In June 2022, Cerebras set a record for the largest AI models ever trained on one device.<ref name=":16">{{Cite web |author1=Francisco Pires |date=2022-06-22 |title=Cerebras Slays GPUs, Breaks Record for Largest AI Models Trained on a Single Device |url=https://www.tomshardware.com/news/cerebras-slays-gpus-breaks-record-for-largest-ai-models-trained-on-a-single-device |access-date=2022-06-22 |website=Tom's Hardware |language=en}}</ref> Cerebras said that for the first time ever,
▲In August 2021, the company announced a system which connects multiple [[integrated circuit]]s (commonly called ''chips'') into a [[neural network]] with many connections.<ref name=":11"/> It enables one system to support AI models with more than 120 trillion [[Parameter (computer programming)|parameters]].<ref name=":12">{{Cite web |last=Khalili |first=Joel |date=25 August 2021 |title=The world's largest chip is creating AI networks larger than the human brain |url=https://www.techradar.com/au/news/the-worlds-largest-chip-is-creating-ai-networks-larger-than-the-human-brain |access-date=2021-09-22 |website=TechRadar |language=en}}</ref>
In August 2022, Cerebras announced that its customers can now train Transformer-style natural language AI models with 20x longer sequences than is possible using traditional computer hardware, which is expected to lead to breakthroughs in [[natural language processing]] (NLP),
▲In June 2022, Cerebras set a record for the largest AI models ever trained on one device.<ref name=":16">{{Cite web |author1=Francisco Pires |date=2022-06-22 |title=Cerebras Slays GPUs, Breaks Record for Largest AI Models Trained on a Single Device |url=https://www.tomshardware.com/news/cerebras-slays-gpus-breaks-record-for-largest-ai-models-trained-on-a-single-device |access-date=2022-06-22 |website=Tom's Hardware |language=en}}</ref> Cerebras said that for the first time ever, one CS-2 system with one Cerebras wafer can train models with up to 20 billion parameters.<ref name=":17">{{Cite news |last=Takahashi |first=Dean |date=2022-06-22 |title=Cerebras Systems sets record for largest AI models ever trained on one device |url=https://venturebeat.com/2022/06/22/cerebras-systems-sets-record-for-largest-ai-models-ever-trained-on-one-device/ |access-date=2022-06-22 |work=VentureBeat |language=en-US}}</ref> The Cerebras CS-2 system can train multibillion-parameter [[natural language processing]] (NLP) models including GPT-3XL 1.3 billion models, [[GPT-J|GPT-J-6B]], GPT-3 13B, and GPT-NeoX 20B with reduced software complexity and infrastructure.<ref name=":17"/><ref name=":16"/>
In September 2022, Cerebras announced that it can patch its chips together to create what would be the largest-ever computing cluster for AI computing.<ref name=":20">{{Cite web |last=Shah |first=Agam |date=2022-09-14 |title=Cerebras Proposes AI Megacluster with Billions of AI Compute Cores |url=https://www.hpcwire.com/2022/09/14/cerebras-proposes-ai-megacluster-with-billions-of-ai-compute-cores/ |access-date=2022-09-14 |website=HPCwire |language=en-US}}</ref> A Wafer-Scale Cluster can connect up to 192 CS-2 AI systems into a cluster, while a cluster of 16 CS-2 AI systems can create a computing system with 13.6 million cores for natural language processing.<ref name=":20" /> The key to the new Cerebras Wafer-Scale Cluster is the exclusive use of data parallelism to train, which is the preferred approach for all AI work.<ref>{{Cite web |last=Freund |first=Karl |title=New Cerebras Wafer-Scale Cluster Eliminates Months Of Painstaking Work To Build Massive Intelligence |url=https://www.forbes.com/sites/karlfreund/2022/09/14/new-cerebras-wafer-scale-cluster-eliminates-months-of-painstaking-work-to-build-massive-intelligence/ |access-date=2022-09-15 |website=Forbes |language=en}}</ref>▼
▲In August 2022, Cerebras announced that its customers can now train Transformer-style natural language AI models with 20x longer sequences than possible using traditional computer hardware, which is expected to lead to breakthroughs in [[natural language processing]] (NLP), especially for pharmaceuticals and life sciences.<ref>{{Cite web |last=Jolly |first=Andrew |title=Cerebras Announces New Capability for Training NLP Models |url=https://www.hpcwire.com/off-the-wire/cerebras-announces-new-capability-for-training-nlp-models/ |access-date=2022-09-01 |website=HPCwire |language=en-US}}</ref>
In November 2022, Cerebras unveiled its latest supercomputer, Andromeda, which combines 16 WSE-2 chips into one cluster with 13.5 million AI-optimized cores, delivering up to 1
▲In September 2022, Cerebras announced that it can patch its chips together to create what would be the largest-ever computing cluster for AI computing.<ref name=":20">{{Cite web |last=Shah |first=Agam |date=2022-09-14 |title=Cerebras Proposes AI Megacluster with Billions of AI Compute Cores |url=https://www.hpcwire.com/2022/09/14/cerebras-proposes-ai-megacluster-with-billions-of-ai-compute-cores/ |access-date=2022-09-14 |website=HPCwire |language=en-US}}</ref> A Wafer-Scale Cluster can connect up to 192 CS-2 AI systems into a cluster, while a cluster of 16 CS-2 AI systems can create a computing system with 13.6 million cores for natural language processing.<ref name=":20"/> The key to the new Cerebras Wafer-Scale Cluster is the exclusive use of data parallelism to train, which is the preferred approach for all AI work.<ref>{{Cite web |last=Freund |first=Karl |title=New Cerebras Wafer-Scale Cluster Eliminates Months Of Painstaking Work To Build Massive Intelligence |url=https://www.forbes.com/sites/karlfreund/2022/09/14/new-cerebras-wafer-scale-cluster-eliminates-months-of-painstaking-work-to-build-massive-intelligence/ |access-date=2022-09-15 |website=Forbes |language=en}}</ref>
In November 2022, Cerebras announced its partnership with [[Cirrascale Cloud Services]] to provide a flat-rate "pay-per-model" compute time for its ''Cerebras AI Model Studio''. Pricing ranges from $2,500 for training "a 1.3-billion-parameter model of GPT-3 in 10 hours" to $2.5 million for training "70-billion-parameter version in 85 days". The service is said to reduce the cost—compared to the similar cloud services on the market—by half while increasing speed up to eight times faster.<ref>{{Cite web |url=https://www.zdnet.com/article/ai-challenger-cerebras-unveils-pay-per-model-large-model-ai-cloud-service-with-cirrascale-jasper/ |title=AI challenger Cerebras unveils 'pay-per-model' AI cloud service with Cirrascale, Jasper |last=Ray |first=Tiernan |date=2022-11-29 |work=ZDNet}}</ref>▼
▲In November 2022, Cerebras unveiled its latest supercomputer, Andromeda, which combines 16 WSE-2 chips into one cluster with 13.5 million AI-optimized cores, delivering up to 1 exa[[FLOPS]] of AI computing power, or at least one quintillion (10{{sup|18}}) operations per second.<ref name=":21">{{Cite web |author1=Paul Alcorn |date=2022-11-14 |title=Cerebras Reveals Andromeda, a 13.5 Million Core AI Supercomputer |url=https://www.tomshardware.com/news/cerebras-reveals-andromeda-a-135-million-core-ai-supercomputer |access-date=2022-11-18 |website=Tom's Hardware |language=en}}</ref><ref>{{Cite news |last=Lee |first=Jane Lanhee |date=2022-11-14 |title=Silicon Valley chip startup Cerebras unveils AI supercomputer |language=en |work=Reuters |url=https://www.reuters.com/technology/silicon-valley-chip-startup-cerebras-unveils-ai-supercomputer-2022-11-14/ |access-date=2022-11-18}}</ref> The entire system uses 500 kilowatts, which is far less power than somewhat-comparable GPU-accelerated supercomputers.<ref name=":21"/>
In 2024 the company introduced WSE-3, a 5nm-based chip hosting 4 trillion transistors and 900,000 AI-optimized cores, the basis of the CS-3 computer along with a collaboration with [[Dell Technologies]].<ref>{{Cite web |last=Kuka |first=Valeriia |last2=Se |first2=Ksenia |date=June 15, 2024 |title=Cerebras: an Engineering Marvel to Rival NVIDIA |url=https://www.turingpost.com/p/cerebras |access-date=2024-06-15 |website=Turing Post |language=en}}</ref>
▲In November 2022, Cerebras announced its partnership with [[Cirrascale Cloud Services]] to provide a flat-rate "pay-per-model" compute time for its ''Cerebras AI Model Studio''. Pricing ranges from $2,500 for training "a 1.3-billion-parameter model of GPT-3 in 10 hours" to $2.5 million for training "70-billion-parameter version in 85 days". The service is said to reduce the cost—compared to the similar cloud services on the market—by half while increasing speed up to eight times faster.<ref>{{Cite web |url=https://www.zdnet.com/article/ai-challenger-cerebras-unveils-pay-per-model-large-model-ai-cloud-service-with-cirrascale-jasper/ |title=AI challenger Cerebras unveils 'pay-per-model' AI cloud service with Cirrascale, Jasper |last=Ray |first=Tiernan |date=2022-11-29 |work=ZDNet}}</ref>
== Deployments ==
Customers are reportedly using Cerebras technologies in the pharmaceutical, life sciences, and energy sectors.<ref name=":9">{{Cite web|date=2020-10-13|title=LLNL, ANL and GSK Provide Early Glimpse into Cerebras AI System Performance|url=https://www.hpcwire.com/2020/10/13/llnl-anl-and-gsk-provide-early-glimpse-into-cerebras-ai-system-performance/|access-date=2021-06-03|website=HPCwire|language=en-US}}</ref><ref name=":14">{{Cite web |date=2022-03-03 |title=Cerebras Systems Supplies 2nd-Gen AI System to TotalEnergies |url=https://www.enterpriseai.news/2022/03/03/cerebras-systems-supplies-2nd-gen-ai-system-to-totalenergies/ |access-date=2022-03-04 |website=EnterpriseAI |language=en-US}}</ref>
In 2020, [[GlaxoSmithKline]] (GSK) began using the Cerebras CS-1 AI system in their London AI hub, for neural network models to accelerate genetic and genomic research and reduce the time taken in [[drug discovery]].<ref>{{Cite web|last=Ray|first=Tiernan|title=Glaxo's biology research with novel Cerebras machine shows hardware may change how AI is done |url= https://www.zdnet.com/article/glaxos-biology-research-with-novel-cerebras-machine-shows-hardware-may-change-how-ai-is-done/ |date= September 5, 2020 |access-date= August 13, 2021 |website= ZDNet|language=en}}</ref> The GSK research team was able to increase the complexity of the encoder models they could generate, while reducing training time.<ref>{{Cite
[[Argonne National Laboratory]] has been using the
Cerebras and the [[National Energy Technology Laboratory]] (NETL) demonstrated record-breaking performance of Cerebras' CS-1 system on a scientific compute workload in November 2020. The CS-1 was 200 times faster than the Joule Supercomputer on the key workload of Computational Fluid Dynamics.<ref>{{Cite web |title=Cerebras Systems and NETL Set New Compute Milestone |url=https://www.hpcwire.com/off-the-wire/cerebras-systems-and-netl-set-new-compute-milestone/ |access-date=2022-03-04 |website=HPCwire |language=en-US}}</ref>
The [[Lawrence Livermore National Laboratory|Lawrence Livermore National Lab]]’s Lassen supercomputer incorporated the CS-1 in both classified and non-classified areas for physics simulations.<ref>{{Cite web|date=2020-08-19|title=Cerebras puts 'world's largest computer chip' in Lassen supercomputer|url=https://venturebeat.com/2020/08/19/cerebras-puts-worlds-largest-computer-chip-in-lassen-supercomputer/|access-date=2021-06-03|website=VentureBeat|language=en-US}}</ref> The [[Pittsburgh Supercomputing Center]] (PSC) has also incorporated the CS-1 in their Neocortex supercomputer for dual HPC and AI workloads.<ref>{{Cite web |last=Hemsoth |first=Nicole |date=2021-03-30 |title=Neocortex Supercomputer to Put Cerebras CS-1 to the Test |url=https://www.nextplatform.com/2021/03/30/neocortex-supercomputer-to-put-cerebras-cs-1-to-the-test/ |access-date=2022-03-04 |website=The Next Platform |language=en-US}}</ref> [[Edinburgh Parallel Computing Centre|EPCC]], the supercomputing center of the University of Edinburgh, has also deployed a CS-1 system for AI-based research.<ref>{{Cite
In August 2021, Cerebras announced a partnership with [https://www.peptilogics.com Peptilogics] on the development of AI for [[peptide therapeutics]].<ref>{{Cite web|title=Peptilogics and Cerebras Systems Partner on AI Solutions to Advance Peptide Therapeutics|url=https://www.hpcwire.com/off-the-wire/peptilogics-and-cerebras-systems-partner-on-ai-solutions-to-advance-peptide-therapeutics/|access-date=2021-09-22|website=HPCwire |language=en-US}}</ref>
=== CS-2 ===
In March 2022, Cerebras announced that the Company deployed its CS-2 system in the Houston facilities of [[TotalEnergies]], its first publicly disclosed customer in the energy sector.<ref name=":14" /> Cerebras also announced that it has deployed a CS-2 system at [https://nference.com nference], a startup that uses natural language processing to analyze massive amounts of biomedical data. The CS-2 will be used to train transformer models that are designed to process information from piles of unstructured medical data to provide fresh insights to doctors and improve patient recovery and treatment.<ref>{{Cite web |title=Cerebras brings CS-2 system to data analysis biz nference |url=https://www.theregister.com/2022/03/14/cerebras_ai_chips/ |access-date=2022-03-15 |website=www.theregister.com |language=en}}</ref>
In May 2022, Cerebras announced that the [[National Center for Supercomputing Applications]] (NCSA) has deployed the Cerebras CS-2 system in their HOLL-I supercomputer.
In October 2022, it was announced that the U.S. National Nuclear Security Administration would sponsor a study to investigate using Cerebras' CS-2 in nuclear stockpile stewardship computing.<ref name=":22">{{Cite web |date=2022-10-18 |title=NNSA Taps 3 Federal Labs to Research Applications of Cerebras Systems Tech - ExecutiveBiz |url=https://blog.executivebiz.com/2022/10/nnsa-taps-3-federal-labs-to-research-applications-of-cerebras-systems-tech/ |access-date=2022-11-18 |website=blog.executivebiz.com |language=en-US}}</ref><ref>{{Cite web |last=Mann |first=Tobias |title=DoE to trial Cerebras AI compute in nuclear weapon sims |url=https://www.theregister.com/2022/10/18/doe_cerebras_waferscale/ |access-date=2022-11-18 |website=www.theregister.com |language=en}}</ref> The multi-year contract will be executed through [[Sandia National Laboratories]], [[Lawrence Livermore National Laboratory|Lawrence Livermore National Lab]], and [[Los Alamos National Laboratory]].<ref name=":22" />
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The 2022 Gordon Bell Special Prize Winner for HPC-Based COVID-19 Research, which honors outstanding research achievement towards the understanding of the COVID-19 pandemic through the use of high-performance computing, used Cerebras' CS-2 system to conduct this award-winning research to transform large language models to analyze COVID-19 variants. The paper was authored by a 34-person team from Argonne National Laboratory, California Institute of Technology, Harvard University, Northern Illinois University, Technical University of Munich, University of Chicago, University of Illinois Chicago, Nvidia, and Cerebras. ANL noted that using the CS-2 Wafer-Scale Engine cluster, the team was able to achieve convergence when training on the full SARS-CoV-2 genomes in less than a day.<ref>{{Cite web |last=Peckham |first=Oliver |date=2022-11-17 |title=Gordon Bell Nominee Used LLMs, HPC, Cerebras CS-2 to Predict Covid Variants |url=https://www.hpcwire.com/2022/11/17/gordon-bell-nominee-used-llms-hpc-cerebras-cs-2-to-predict-covid-variants/ |access-date=2022-11-23 |website=HPCwire |language=en-US}}</ref><ref>{{Cite web |last=Peckham |first=Oliver |date=2022-11-17 |title=Gordon Bell Special Prize Goes to LLM-Based Covid Variant Prediction |url=https://www.hpcwire.com/2022/11/17/gordon-bell-special-prize-goes-to-llm-based-covid-variant-prediction/ |access-date=2022-11-23 |website=HPCwire |language=en-US}}</ref>
Cerebras partnered with Emirati technology group [[G42 (company)|G42]] to deploy its AI supercomputers to create chatbots and to analyze genomic and preventive care data. In July 2023, G42 agreed to pay around $100 million to purchase the first of potentially nine supercomputers from Cerebras, each of which capable of 4 [[FLOPS|exaflops]] of compute.<ref>{{cite news |last=Nellis |first=Stephen |last2=Hu |first2=Krystal |date=20 July 2023 |title=Cerebras Systems signs $100 mln AI supercomputer deal with UAE's G42 |url=https://www.reuters.com/technology/cerebras-systems-signs-100-mln-ai-supercomputer-deal-with-uaes-g42-2023-07-20/ |publisher=Reuters}}</ref><ref>{{cite news |last=Lu |first=Yiwen |date=20 July 2023 |title=An A.I. Supercomputer Whirs to Life, Powered by Giant Computer Chips |url=https://www.nytimes.com/2023/07/20/technology/an-ai-supercomputer-whirs-to-life-powered-by-giant-computer-chips.html |work=The New York Times}}</ref><ref>{{cite web |last=Moore |first=Samuel K. |date=20 July 2023 |title=Cerebras Introduces Its 2-Exaflop AI Supercomputer |url=https://spectrum.ieee.org/ai-supercomputer-2662304872 |work=IEEE Spectrum}}</ref> In August 2023, Cerebras, the [[Mohamed bin Zayed University of Artificial Intelligence]] and G42 subsidiary Inception launched [[Jais (language model)|Jais]], a [[large language model]].<ref name=":02">{{Cite news |last=Cherney |first=Max A. |date=2023-08-30 |title=UAE's G42 launches open source Arabic language AI model |language=en |work=[[Reuters]] |url=https://www.reuters.com/technology/uaes-g42-launches-open-source-arabic-language-ai-model-2023-08-30/ |access-date=2023-10-08}}</ref>
[[Mayo Clinic]] announced a collaboration with Cerebra’s at the 2024 [[J.P. Morgan & Co.|J.P. Morgan Healthcare Conference]], offering details on the first foundation model it will develop with the enablement of Cerebras's generative AI computing capability. The solution will combine genomic data with de-identified data from patient records and medical evidence to explore the ability to predict a patient's response to treatments to manage disease and will initially be applied to [[rheumatoid arthritis]]. The model could serve as a prototype for similar solutions to support the diagnosis and treatment of other diseases.
== See also ==
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== References ==
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== External links ==
* {{Official
* [https://www.servethehome.com/cerebras-wafer-scale-engine-wse-2-and-cs-2-at-hot-chips-34/ Cerebras' presentation at Hot Chips 34 (2022)]
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[[Category:Companies based in Sunnyvale, California]]
[[Category:Companies based in Silicon Valley]]
[[Category:Computer hardware companies]]
[[Category:Semiconductor companies of the United States]]
[[Category:Fabless semiconductor companies]]
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