Editing Nvidia (section)

== Deep learning ==
Nvidia GPUs are used in [[deep learning]], and accelerated analytics due to Nvidia's [[CUDA]] software platform and API which allows programmers to utilize the higher number of cores present in GPUs to [[Parallel computing|parallelize]] [[BLAS]] operations which are extensively used in [[machine learning]] algorithms.<ref name="Elsevier" /> They were included in many Tesla, Inc. vehicles before Musk announced at Tesla Autonomy Day in 2019 that the company developed its own SoC and full self-driving computer now and would stop using Nvidia hardware for their vehicles.<ref>{{Cite web |date=August 13, 2020 |title=Nvidia's Self-Driving Vehicle Approach — from Tesla to DHL to Mercedes |url=https://cleantechnica.com/2020/08/13/nvidias-self-driving-vehicle-approach-from-tesla-to-dhl-to-mercedes/ |access-date=October 28, 2020 |website=CleanTechnica |language=en-US |archive-date=January 17, 2021 |archive-url=https://web.archive.org/web/20210117215917/https://cleantechnica.com/2020/08/13/nvidias-self-driving-vehicle-approach-from-tesla-to-dhl-to-mercedes/ |url-status=live}}</ref><ref>{{Cite web |title=Google Cloud adds NVIDIA Tesla K80 GPU support to boost deep learning performance – TechRepublic |date=February 22, 2017 |url=http://www.techrepublic.com/article/google-cloud-adds-nvidia-tesla-k80-gpu-support-to-boost-deep-learning-performance/ |access-date=April 19, 2017 |archive-date=June 10, 2019 |archive-url=https://web.archive.org/web/20190610173053/https://www.techrepublic.com/article/google-cloud-adds-nvidia-tesla-k80-gpu-support-to-boost-deep-learning-performance/ |url-status=live}}</ref> These GPUs are used by researchers, laboratories, tech companies and enterprise companies.<ref>{{Cite web |title=Intel, Nvidia Trade Shots Over AI, Deep Learning |date=August 25, 2016 |url=http://www.eweek.com/servers/intel-nvidia-trade-shots-over-ai-deep-learning.html|access-date=April 19, 2017|archive-date=May 21, 2023|archive-url=https://web.archive.org/web/20230521130755/https://www.eweek.com/servers/intel-nvidia-trade-shots-over-ai-deep-learning/|url-status=live}}</ref> In 2009, Nvidia was involved in what was called the "big bang" of deep learning, "as deep-learning neural networks were combined with Nvidia graphics processing units (GPUs)".<ref>{{Cite web |date=April 5, 2016 |title=Nvidia CEO bets big on deep learning and VR |url=https://venturebeat.com/2016/04/05/nvidia-ceo-bets-big-on-deep-learning-and-vr/ |access-date=April 19, 2017 |archive-date=November 25, 2020 |archive-url=https://web.archive.org/web/20201125202428/https://venturebeat.com/2016/04/05/nvidia-ceo-bets-big-on-deep-learning-and-vr/ |url-status=live}}</ref> That year, the [[Google Brain]] team used Nvidia GPUs to create [[deep neural networks]] capable of machine learning, where [[Andrew Ng]] determined that GPUs could increase the speed of deep learning systems by about 100 times.<ref>{{Cite news |title=From not working to neural networking |newspaper=The Economist |date=June 23, 2016 |url=https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not|access-date=September 7, 2017|archive-date=December 31, 2016|archive-url=https://web.archive.org/web/20161231203934/https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not|url-status=live}}</ref>

=== DGX ===
{{Main|Nvidia DGX}}
DGX is a line of [[supercomputers]] by Nvidia.

In April 2016, Nvidia produced the [[DGX-1]] based on an 8 GPU cluster, to improve the ability of users to use deep learning by combining GPUs with integrated deep learning software.<ref>{{Cite web |last=Coldewey |first=Devin |title=NVIDIA announces a supercomputer aimed at deep learning and AI |date=April 5, 2016 |url=https://techcrunch.com/2016/04/05/nvidia-announces-a-supercomputer-aimed-at-deep-learning-and-ai/ |access-date=September 7, 2017 |archive-date=November 29, 2020 |archive-url=https://web.archive.org/web/20201129054127/https://techcrunch.com/2016/04/05/nvidia-announces-a-supercomputer-aimed-at-deep-learning-and-ai/ |url-status=live}}</ref> Nvidia gifted its first DGX-1 to [[OpenAI]] in August 2016 to help it train larger and more complex AI models with the capability of reducing processing time from six days to two hours.<ref>{{Cite news |last1=Carr |first1=Austin |last2=King |first2=Ian |date=June 15, 2023 |title=How Nvidia Became ChatGPT's Brain and Joined the $1 Trillion Club |url=https://www.bloomberg.com/news/features/2023-06-15/nvidia-s-ai-chips-power-chatgpt-and-multibillion-dollar-surge |publisher=Bloomberg News |url-access=subscription |archive-url=https://archive.today/20230618072913/https://www.bloomberg.com/news/features/2023-06-15/nvidia-s-ai-chips-power-chatgpt-and-multibillion-dollar-surge |archive-date=June 18, 2023 |url-status=live}}</ref><ref>{{Cite news |last=Vanian |first=Jonathan |date=August 15, 2016 |title=Elon Musk's Artificial Intelligence Project Just Got a Free Supercomputer |url=https://fortune.com/2016/08/15/elon-musk-artificial-intelligence-openai-nvidia-supercomputer/ |work=Fortune |url-access=subscription |archive-url=https://archive.today/20230607233501/https://fortune.com/2016/08/15/elon-musk-artificial-intelligence-openai-nvidia-supercomputer/ |archive-date=June 7, 2023 |url-status=live}}</ref> It also developed Nvidia Tesla K80 and P100 GPU-based virtual machines, which are available through [[Google Cloud Platform|Google Cloud]], which Google installed in November 2016.<ref>{{Cite web |last=Nichols |first=Shaun |date=February 21, 2017 |title=Google rents out Nvidia Tesla GPUs in its cloud. If you ask nicely, that'll be 70 cents an hour, bud |url=https://www.theregister.co.uk/2017/02/21/google_says_cloud_gpu_boxes_are_go/ |website=[[The Register]] |access-date=April 19, 2017 |archive-date=July 9, 2019 |archive-url=https://web.archive.org/web/20190709170955/https://www.theregister.co.uk/2017/02/21/google_says_cloud_gpu_boxes_are_go/ |url-status=live}}</ref> [[Microsoft]] added GPU servers in a preview offering of its N series based on Nvidia's Tesla K80s, each containing 4992 processing cores. Later that year, AWS's P2 instance was produced using up to 16 Nvidia Tesla K80 GPUs. That month Nvidia also partnered with IBM to create a software kit that boosts the AI capabilities of [[Watson (computer)|Watson]],<ref>{{Cite web |title=IBM, NVIDIA partner for 'fastest deep learning enterprise solution' in the world – TechRepublic |date=November 14, 2016 |url=http://www.techrepublic.com/article/ibm-nvidia-partner-for-fastest-deep-learning-enterprise-solution-in-the-world/ |access-date=April 19, 2017 |archive-date=October 12, 2020 |archive-url=https://web.archive.org/web/20201012034603/https://www.techrepublic.com/article/ibm-nvidia-partner-for-fastest-deep-learning-enterprise-solution-in-the-world/ |url-status=live}}</ref> called IBM PowerAI.<ref>{{Cite web |date=November 14, 2016 |title=IBM and Nvidia team up to create deep learning hardware |url=https://venturebeat.com/2016/11/14/ibm-and-nvidia-team-up-to-create-deep-learning-hardware/ |access-date=April 19, 2017 |archive-date=October 12, 2020 |archive-url=https://web.archive.org/web/20201012090856/https://venturebeat.com/2016/11/14/ibm-and-nvidia-team-up-to-create-deep-learning-hardware/ |url-status=live}}</ref><ref>{{Cite web |date=November 15, 2016 |title=IBM and Nvidia make deep learning easy for AI service creators with a new bundle |url=http://www.digitaltrends.com/computing/ibm-nvidia-hardware-software-bundle-deep-learning-ai/ |access-date=April 19, 2017 |archive-date=October 24, 2020 |archive-url=https://web.archive.org/web/20201024185627/https://www.digitaltrends.com/computing/ibm-nvidia-hardware-software-bundle-deep-learning-ai/ |url-status=live}}</ref> Nvidia also offers its own Nvidia Deep Learning software development kit.<ref>{{Cite web |title=Facebook 'Big Basin' AI Compute Platform Adopts NVIDIA Tesla P100 For Next Gen Data Centers |url=http://hothardware.com/news/facebook-big-basin-ai-platform-adopts-nvidia-tesla-p100-data-centers |access-date=April 19, 2017 |archive-date=November 26, 2020 |archive-url=https://web.archive.org/web/20201126093601/https://hothardware.com/news/facebook-big-basin-ai-platform-adopts-nvidia-tesla-p100-data-centers |url-status=dead}}</ref> In 2017, the GPUs were also brought online at the [[Riken]] Center for Advanced Intelligence Project for [[Fujitsu]].<ref>{{Cite web |date=March 5, 2017 |title=Nvidia to Power Fujitsu's New Deep Learning System at RIKEN – insideHPC |url=http://insidehpc.com/2017/03/nvidia-power-fujitsus-new-deep-learning-system-riken/ |access-date=April 19, 2017 |archive-date=October 24, 2020 |archive-url=https://web.archive.org/web/20201024004640/https://insidehpc.com/2017/03/nvidia-power-fujitsus-new-deep-learning-system-riken/ |url-status=live}}</ref> The company's deep learning technology led to a boost in its 2017 earnings.<ref>{{Cite web |last=Tilley |first=Aaron |date=February 9, 2017 |title=Nvidia Beats Earnings Estimates As Its Artificial Intelligence Business Keeps On Booming |url=https://www.forbes.com/sites/aarontilley/2017/02/09/nvidia-beat-earnings-estimates-as-its-artificial-intelligence-business-keeps-on-booming/ |access-date=January 27, 2021 |website=[[Forbes]] |language=en |archive-date=April 19, 2017 |archive-url=https://web.archive.org/web/20170419202205/https://www.forbes.com/sites/aarontilley/2017/02/09/nvidia-beat-earnings-estimates-as-its-artificial-intelligence-business-keeps-on-booming/ |url-status=live}}</ref>

In May 2018, researchers at the artificial intelligence department of Nvidia realized the possibility that a robot can learn to perform a job simply by observing the person doing the same job. They have created a system that, after a short revision and testing, can already be used to control the universal robots of the next generation. In addition to GPU manufacturing, Nvidia provides parallel processing capabilities to researchers and scientists that allow them to efficiently run high-performance applications.<ref>[https://newatlas.com/robot-learn-watching-humans/54732/ "Robot see, robot do: Nvidia system lets robots learn by watching humans"] {{Webarchive|url=https://web.archive.org/web/20201109004513/https://newatlas.com/robot-learn-watching-humans/54732/ |date=November 9, 2020 }} ''New Atlas'', May 23, 2018</ref>

=== Robotics ===
In 2020, Nvidia unveiled "[[Nvidia Omniverse|Omniverse]]", a virtual environment designed for engineers.<ref>{{Cite web |last=Takahashi |first=Dean |date=October 5, 2020 |title=Nvidia announces open beta for Omniverse as a 'metaverse' for engineers |url=https://venturebeat.com/ai/nvidia-announces-open-beta-for-omniverse-as-a-metaverse-for-engineers/ |access-date=June 19, 2024 |website=VentureBeat |language=en-US}}</ref> Nvidia also open-sourced Isaac Sim, which makes use of this Omniverse to train robots through simulations that mimic the physics of the robots and the real world.<ref>{{Cite web |last=Takahashi |first=Dean |date=January 3, 2023 |title=Nvidia advances robot simulation with updates to Isaac Sim |url=https://venturebeat.com/ai/nvidia-advances-robot-simulation-with-updates-to-isaac-sim/ |access-date=June 19, 2024 |website=VentureBeat |language=en-US}}</ref><ref>{{Cite web |title=NVIDIA Isaac Sim |url=https://github.com/isaac-sim |access-date=June 19, 2024 |website=GitHub |language=en}}</ref>

In 2024, Huang oriented Nvidia's focus towards [[humanoid robot]]s and [[self-driving car]]s, which he expects to gain widespread adoption.<ref>{{Cite web |last=Morris |first=Meghan |date=June 3, 2024 |title=Nvidia CEO Jensen Huang says robots are the next wave of AI — and 2 kinds will dominate |url=https://www.businessinsider.com/nvidia-ceo-jensen-huang-pushes-robot-revolution-taiwan-computex-speech-2024-6 |access-date= |website=Business Insider |language=en-US}}</ref><ref>{{Cite web |last=Coleman |first=Julie |date=March 20, 2024 |title=Nvidia CEO Jensen Huang explains why he's all in on humanoid robotics |url=https://www.cnbc.com/2024/03/20/nvidia-ceo-jensen-huang-explains-why-hes-all-in-on-humanoid-robotics.html |access-date=June 19, 2024 |website=CNBC |language=en}}</ref>

In 2025, Nvidia announced Isaac GR00T N1, an open-source, [[foundation model]] "designed to expedite the development and capabilities of humanoid robots". [[Neura Robotics]], [[1X Technologies]] and [[Vention]] are among the first companies to use the model.<ref>{{Cite web |last=Liszewski |first=Andrew |date=2025-03-18 |title=Nvidia says 'the age of generalist robotics is here' |url=https://www.theverge.com/news/631743/nvidia-issac-groot-n1-robotics-foundation-model-available |access-date=2025-04-07 |website=The Verge}}</ref><ref>{{Cite web |last=Moore |first=Mike |date=2025-03-18 |title='The age of generalist robotics is here' - Nvidia's latest GROOT AI model just took us another step closer to fully humanoid robots |url=https://www.techradar.com/pro/the-age-of-generalist-robotics-is-here-nvidias-latest-groot-ai-model-just-took-us-another-step-closer-to-fully-humanoid-robots |access-date=2025-04-07 |website=TechRadar}}</ref><ref>{{cite magazine |title=How Nvidia's Simulation Tech is Used for Advanced Robotics Training |magazine=[[Automation World]] |date=January 8, 2025 |url=https://www.automationworld.com/factory/robotics/news/55253285/how-nvidias-simulation-tech-is-used-for-advanced-robotics-training}}</ref>