Nvidia’s BREAKTHROUGH GPU Trains ChatGPT in MINUTES! (SIGGRAPH Keynote 2023)

in 2018 five years ago this was the Showcase demo the first RTX GPU was called touring as you can imagine we did it on purpose it was appropriately named to unify computer graphics and artificial intelligence for the very first time this demo was called Star Wars reflection it was created by the researchers at l m X labs epic and Nvidia it had two and a half million polygons or so two rays per pixel a couple of bounces per Ray did ambient inclusion area lights specular reflections it was a hybrid rasterization and Ray Trace demo we rendered it at 720P 30 frames a second and we use dlss super resolution to scale it to 4K the demonstration was frankly at the time incredibly beautiful that was five years ago now five years later racer RTX 250 million polygons a hundred times more geometry 10 raise per pixel about 10 bounces per Ray we're using a unified lighting system for every effect for the very first time this entire scene is completely Pat traced no rasterization we're rendering it at 1080p 30 Hertz and using dlss using artificial intelligence infer something like one out of infer like seven out of eight pixels Computing only one out of eight and as a result we're able to render this at 4K scale it up to 4K 60 4K 30 Hertz hit it I think it's safe to say that it was worth it to bet the company we realized that rasterization was reaching its limits and unless we did took such a giant risk again and introduced a brand new way of doing computer Graphics combining CG and AI for the very first time what you just saw would not be possible modern computer Graphics has been reinvented the BET has paid off while we were Reinventing computer graphics with artificial intelligence we were Reinventing the GPU all together for artificial intelligence the GPU when I came to see you last time five years ago most people would say that this is what a GPU looks like and in fact this is the GPU that we announced this is touring and uh as you guys might remember this this is the touring GPU but this is what a GPU is today this GPU is um I guess uh let's see eight hoppers each one of them all together something like between The Hoppers the eight Hoppers connected with MV link the infiniband networking the MV switches that are connecting them together the Envy link switches all together one trillion transistors this GPU has 35 000 parts it's manufactured by a robot like an electric car it weighs 70 pounds consumes 6000 Watts and this GPU revolutionized computer science altogether after 12 years working on artificial intelligence something gigantic happened degenerative AI era is upon us the iPhone moment of AI if you will where all of the Technologies of artificial intelligence came together in such a way that is now possible for us to enjoy AI in so many different applications the Revolutionary Transformer model allows us to learn from a large amount of data that's across large spans of space and time to find patterns and relationships to learn the representation of almost anything with structure we learned a representation how to represent language in mathematics and vectors in Vector space audio animation 3D video DNA proteins chemicals and with a generative model and the Learned language model you can guide the Auto regressive diffusion models to generate almost anything you like and so we could learn the representation of almost anything with structure we can generate almost anything that we can learn from structure and we can guide it with our human natural language the Journey of Nvidia accelerated Computing met the Journey of the deep learning researchers and the Big Bang of modern AI happened this is now 12 years later the 12-year journey of our work in artificial intelligence and it is incredible what is happening around the world the generative AI era has clearly started the combination of large language models and generative models these Auto regressive generative models has kicked off degenerative AI era thousands of papers in just the last several years have been written about this area of large language models and generative AI billions of dollars are being invested into companies and just about every single domain and every single industry is pursuing ideas on generative Ai and the reason for that is very simple the single most valuable thing that we do as humanity is to generate intelligent information and now for the very first time computers can help us augment our ability to generate information what's really profound though is that when you take a step back and ask yourself what is the meaning of generative AI why is this such a big deal why is it changing everything well the reason for that is first human is the new programming language we've democratized computer science everybody can be a programmer now because human language natural language is the best programming language and it's the reason why chat GPT has been so popular everybody can program that computer large language model is a new Computing platform because now the programming language is human and what you program the computer understands larger language models and generative AI is the new killer app these three insights has gotten everybody just insanely excited and because for the very first time after 15 years or so a new Computing platform has emerged like the PC like the internet like mobile cloud computing a new Computing platform has emerged and this new Computing platform is going to enable all kinds of new applications but very differently than the past this new Computing platform benefits every single Computing platform before it while general purpose Computing is a horrible way of doing generative Ai and you can see that in just a second and so we created a brand new processor for the era of generative Ai and this is it this is the Grace Hopper we announced Grace Hopper in fact just only recently several months ago and today we're announcing that we're going to give it a boost we're going to give this processor a boost with the world's fastest memory called hbm3e the world's fast memory fastest memory now connected to Grace Hopper we're calling it gh200 the chips are in production we'll sample it at the end of the year or so and be in production by by the end of second quarter this processor is designed for scale out of the world's data centers has 72 cores great CPU cores connected through this incredibly high speed link cash coherent memory coherent link between the CPU and the GPU this is the CPU and that's the GPU the hopper GPU is now connected to hbm3e it has four petaflops of Transformer engine processing capability and now it has five terabytes per second of hbn 3E performance so this is the new G h200 based on the architecture Grace Hopper and a processor for this new Computing era there's a whole lot of ways that we can connect Grace Hopper into a computer this is one of my favorites by connecting two of them into one Computing node connecting it together with mvlink and this MV link between these between these two processor modules is six terabytes per second and it basically turns these two these two processors these two Super Chips into a super sized super chip one giant GPU one giant CPU the CPU now has 144 cores the GPU has 10 terabytes per second of frame buffer bandwidth 10 terabytes per second frame buffer bandwidth and 282 gigabytes of hbm3e well pretty much you could take just about any large language model you like and put it into this and it will inference like crazy but what if we would like to scale this up into a much much larger GPU run it please all right foreign [Applause] this is actual size by the way this is actual size and it probably even runs crisis the world's largest single GPU one exoflops four petaflops per grasshopper 256 connected by mvlink into one giant system and so this is a modern GPU so next time when you order a GPU on Amazon don't be surprised if this shows up general purpose of computing is going to give way to accelerated Computing and AI Computing and let me illustrate to you why the canonical use case of the future is a large language model in the front end of just about everything every single application every single database whenever you interact with an apple with a computer it will likely be first you'll likely be first engaging a large language model that large language models of will figure out what is your intention what is your desire what are you trying to do given the context and present the information to you in the best possible way it would do the SMART query maybe a smart search augment that query and search with your question with your prompt and generate whatever information necessary and so the canonical example that I'm using here is a llama2 large language model that is being inferenced it then does a query into a semantic database a vector database of some kind and the output of that is augmented and becomes a guide for a generative model and here the generative model I'm using is stable diffusion Excel and so these three models number two Vector database and stable diffusion sdxl are relatively well understood as state-of-the-art and the type of models that you could imagine running just by everywhere well if you were to have an ISO budget way of processing that workload it would take let me just choose the number of 100 million dollars and 100 million dollars would be a reasonably small data center these days 100 million dollars will buy you about 8 800 x86 gpus it would take about five megawatts to operate that and I normalized the performance into 1X using the exact same budget with accelerated Computing Grace Hopper it would consume only three megawatts but your throughput goes up by an order of magnitude basically the Energy Efficiency the cost efficiency of accelerated Computing for generative AI applications is about 20x 20x in Moore's Law and just the current way of scaling CPUs that would be a very very long time and so this is a giant Step Up in efficiency and throughput so this is ISO budget let's take a look at this now again and let's go through ISO workload suppose your intention was to provide a service and that service has so many number of users and so your workload is fairly well understood plus or minus and so with ISO workload this 1X 100 million dollars using general purpose computing and using accelerated Computing Grace Hopper it would only cost eight million dollars eight million dollars and only 260 not megawatts yeah 0.26 260 kilowatts so 20 times less power and 12 times less cost this is the reason why accelerated Computing is going to be the path forward and this is the reason why the world's data centers are very quickly transitioning to accelerated Computing and some people say and you guys might have heard I don't know who said it but the more you buy the the more you save and and that's that's wisdom if I could just ask you to remember one thing from my talk today uh that that would really be it that it's that the future is accelerated Computing and the more you buy the more you save today I want to talk about Omniverse and generative Ai and how they come together the first thing that we already established is that graphics and artificial intelligence are inseparable that Graphics needs Ai and AI needs Graphics Graphics needs Ai and AI needs graphics and so the first thing that that you could imagine doing for the future of artificial intelligence is the teacher common sense all of us understand the consequences of the physical actions we take all of us understand that gravity has effect and all of us understand that even though you don't see something that object might still be there probably is still there physical object presence and so that that common sense is known to humans ever since you're babies and yet for most artificial intelligence agents that learned on large language models it's unlikely it has that common sense that object permanence the effects of gravity the consequence of your actions you have to learn it in a physically grounded way and so the thing that we could do is we could create a virtual world that is physically simulated physics simulator that allows an artificial intelligence to learn how to perceive the environment using a vision Transformer Maybe and to use reinforcement learning to understand the impacts the consequences consequences of its physical actions and learn how to animate and learn how to articulate to achieve a particular goal and so one mission of a connected artificial intelligence system and a virtual world system that we call Omniverse is so that the future of AI could be physically grounded the number of applications is really quite exciting because as we know the largest Industries in the world are heavy industry and those heavy Industries are physics-based physically based and so first application is so that AI can learn in a virtual world the second application the second reason why AI is and computer Graphics are inseparable is that AI will help also to create these Virtual Worlds let me give you a couple of examples this is a AI that is a large language model as I mentioned that will be connected to almost every single application however you the future user interface of almost every application is a large language model and so it's sensible to imagine that this large language model could also be the query front end to a 3D database and so here I find a denza N7 SUV now once you find this SUV you might ask an AI agent to help you to turn this car to embed this car to integrate this car into a virtual environment and instead of Designing that virtual environment you ask the AI to help you give me a road in the desert at sunset now inside Omniverse we can then unify aggregate composite these two this information together and now the car is integrated rendered into positioned into a virtual world and so here's an AI that helps you maybe create and find some manage your data assets you also have an AI that helps you generate a virtual world around it and Omniverse allows you to integrate all this information well let's take a look at what wpp the world's largest Ad Agency and byd the world's largest electric vehicle maker are used how they're using Omniverse and generative AI in their work play it please wpp is building the next generation of car configurators for automotive giant byds Denzel luxury brand powered by Omniverse cloud and generative AI open USD and Omniverse Cloud allows denza to connect High Fidelity data from industry-leading CAD tools to create a physically accurate real-time digital twin of its N7 wpp artists can work seamlessly on this model in the same Omniverse Cloud environment with their preferred tools from Autodesk Adobe and side effects to deliver the next era of automotive digitalization and immersive experiences today's configurators require hundreds of thousands of images to be pre-rendered to represent all possible options and variants open USD makes it possible for wpp to create a super digital twin of the card that includes all possible variants in one single asset deployed as a fully interactive 3D configurator on Omniverse Cloud gdn a network that can stream High Fidelity real-time 3D experiences to devices in over 100 regions were used to generate thousands of individual pieces of content that comprise a global marketing campaign the USD model is placed in a 3D environment that can either be scanned from The Real World using lidar and virtual production or created in seconds with generative AI tools from organizations such as Adobe and Shutterstock this Innovative wpp solution for byd brings generative Ai and cloud-rendered real-time 3D together for the first time powering the next generation of e-commerce God I love that everything everything was rendered in real time nothing was pre-rendered every single every single scene that you saw was rendered in real time every car all the beautiful integration with the background all the all rendering everything is 100 real time the car is the original CAD data set of vyd nothing was changed you literally take the cad drag it into Omniverse you tell an AI synthesize and generate an environment and all of a sudden the car appears wherever you like it to be so this is a one example of how generative Ai and human designs come together to create these incredible and these incredible applications So today we're announcing that hugging face is going to build a new service to enable their Community to train directly on Nvidia dgx cloud Nvidia djx cloud is the best way to train models and its footprint is being set up our djx Cloud Footprints are being set up in Azure oci Oracle cloud and gcp so the footprint is going to be largely everywhere and you'll be able to find from the hugging face portal choose your model that you would like to train or you like to train a brand new model and connect yourself to dgx Cloud for training so this is going to be a brand new service to connect the world's largest AI Community with the world's best AI training infrastructure so that's number one so the second thing we're announcing today is the Nvidia AI workbench this workbench is a collection of tools that make it possible for you to assemble to automatically assemble the dependent run times in libraries the libraries to help you fine-tune and guard rail to optimize your your large language model as well as assembling all of the acceleration libraries which are so complicated so that you could run it very easily on your target device you could Target a PC you could Target a workstation you could part Target your own data center or with one click you can migrate the entire project into any one of these different areas let's take a look at Nvidia AI workbench in action generative AI is incredibly powerful but getting accurate results customized with your secured proprietary data is challenging Nvidia AI workbench streamlines selecting Foundation models building your project environment and fine-tuning these models with domain-specific data here AI workbench is installed on a GeForce RTX 4090 laptop where we've been experimenting with an sdxl project as our project gets more complex we need much more memory in compute power so we use AI workbench to easily scale to a workstation powered by four Nvidia RTX 6000 Ada generation gpus AI workbench automatically creates your Project's environment building your container with all dependencies including Jupiter now in the jupyter notebook we prompt our model to generate a picture of toy Jensen in space but because our model has never seen toy Jensen it creates an irrelevant result to fix this we fine-tune the model with 8 images of toy Jensen then prompt again the result is much more accurate then with AI workbench we deploy the new model in our Enterprise application [Music] this same simple process can be applied when customizing llms such as Lama 2 70b to accommodate this much larger model we use AI workbench to scale to the data center accessing a server with eight Nvidia l40s gpus we tune with 10 000 USD code Snippets and nearly 30 000 USD functions built by Nvidia which teaches the model to understand 3D USD based scenes we call our new model chat USD chat USD is a USD developers copilot helping answer questions and generate USD python code with Nvidia AI workbench you can easily scale your generative AI projects from laptop to workstation to Data Center or cloud with a few clicks everybody can do this just have to come to our website download Nvidia AI workbench anybody could do this so everybody can do this come to the website uh Early Access download AI workbench it for the creator of the project uh you it helps you set up set up the libraries and the runtimes that you need you can fine tune the model if you want to migrate this this uh project so that all of your colleagues could use it and fine tune other models you could just tell it where you want to migrate it to and one one click it'll migrate the entire dependency of the project all the runtimes all the libraries all the complexities and it runs on workstations and runs in the data center it runs in the cloud one single body of code one single project allows you to run literally everywhere and so everybody can be a generative AI practitioner well what makes it possible to do all this is this other piece of code called Nvidia AI Enterprise this is essentially the operating system of modern data science and modern AI this operating system of AI if you will has been integrated into the cloud integrated with leading operating systems like Linux and windows wsl2 windows subsystem for Linux the second version wsl2 has been optimized for Cuda and uh supports VMware the body of work that we've done with VMware is incredible this took several years to do a couple two and a half years for us to make VMware be Cuda compatible Cuda aware multi-gpu aware and still have all the benefits of an Enterprise wine paint of glass resilient virtualized Data Center and so this entire stack of Nvidia AI Enterprise this is really the giant body of work that makes all of this possible as a result literally everything that you would like to run will be supported by the ecosystem we're talking talking about here wouldn't it be great if you had a powerful machine under your desk and so today we're announcing our latest generation Ada GPU Ada Lovelace GPU the most powerful GPU we've ever put in the workstation is now oh gosh darn it I just put my fingerprints on there did you guys can you guys see that that was what that's not me could you again hey can I have this cleaned in the future my bad yuck let me show that's the worst product launch ever you guys the CEO pulls it out goes yuck [Laughter] data set version of Ada Lovelace sorry everybody my bad they work so hard they worked so hard it was it was so it was perfect it's like beautifully lacquered like I'm sad I'm super sad I'm super sad okay anyways thank you thank you and that's why you should rehearse all right it goes into it goes into these amazing workstations and these amazing workstations um packs up to four of these gpus it packs up to four Nvidia RTX 6000 is the most powerful gpus ever ever created and it run real-time Ray tracing for Omniverse as well as train fine tune and inference large language models for generative Ai and it's available from box and Dell and HPI and Lambda and Lenovo and it's available now another incredible machine are the servers and these servers as you know getting gpus in the cloud these days is no easy feat and now you can buy it okay you could have your your company buy it for you and put it in the data center and there's a whole bunch of these servers a whole bunch of different configurations I don't know if you guys could see this this is a server that has up to eight of uh the l40s Ada Lovelace gpus and of course these are not going to be used for Frontier models these are not designed to train large Frontier models like gpt4 or gpt5 these are really used for mainstream models today that you can download from hugging face or Nvidia could work with your company to create based on our language model called Nemo we could create models that are mainstream today that you could use in just about all kinds of applications around your company and you can fine tune it with these gpus the fine tuning of a gpt3 model okay so this is GPT 340 billion parameters takes about seven hours for about a billion tokens and so 15 hours in a workstation with four GPS of course takes less with hepus and just in fine tuning this is one and a half times faster than our last generation a100 and so l40s is a really terrific GPU for Enterprise scale fine-tuning of mainstream large language models you can also use it for course synthesizing and generating images open USD is a framework a universal interchange for creating 3D worlds for describing for compositing for simulating for collaborating on 3D projects open USD is going to bring together the world onto one standard 3D interchange and has the opportunity to do for the world and for computing what HTML did for the 2D web could you imagine if every single tool was natively compatible with USD then as a result data gravitates to the center everybody who can work in parallel The Interchange and conversion goes away and instead of a serialized model you have a paralyzed spoken Hub model and so this way of doing work of course is incredibly appealing and is one of the reasons why the vision of openusd has taken off well there's some 50 tools available now the industry loves the vision open USD already has a rich ecosystem some 50 tools are now compatible with openusd natively 170 contributors in the USD forum from about 100 companies so you got a lot of people really interested in this and the momentum is growing it's being adopted in film architecture engineering and construction manufacturing and so many different fields of Robotics well five years ago we started working with Pixar and we adopted USD as the foundation of Omniverse our vision was to create these Virtual Worlds that make it possible for us to bring World design into the applications that I mentioned and so Omniverse was designed to connect it's not a tool in itself it's a connector of tools it's not intended to be a final Production Tool it's intended to make be a connector that make it possible for everybody to collaborate interchange share live work okay so Omniverse is a connector well let's take a look at how the vision of open USD came came together and this is just a fantastic illustration let's starting from the left here I think this is a Adobe Stager Houdini this is a modeling system Maya or animation system modeling system this is Omniverse blender render man Pixar's Minuteman and Unreal Engine from epic a game engine literally all open USD one data set ingested into everybody's tools and it looks basically the same everybody's rendering system is a little different and so the quality of the rendering is is a little different from tool to Tool but one data set available and usable by every tool this is the vision of open USD so incredibly powerful well we've been investing in USD now for over five years this graph is the five if you will we've been working on USD now for about five years the sigrap and we've been working on extending USD to real-time and physics-based systems for industrial applications we brought RTX to it we bought we extended USD with with a schema for physics real-time physics and offline physics we added CAD to USD connect USD to a whole new industry we made it possible to understand geospatial data to recognize and understand comprehend uh consider the curvature of the Earth um we integrated it with a AI runtime as well as a framework to build generative AI for example the Deep search that we showed you chat GPT or chat USD that I'll show you in just a second we created a we extended USD for assets that are physics accurate physics aware so we call it Sim ready it's particularly interesting particularly important for robotics applications so that the joints move accordingly and such and and we took USD and made it hyperscale so that we can expand it and grow it make it support data sets of enormous scale and put it in the cloud connected it with openxr and reality kit so that we can stream from the cloud to spatial Computing devices well for the last five years we've been working on Omniverse has been building and working collaborating with the industry on USD let's take a look at this everything you're about to see is a simulation everything is real time and so take a look at this this is the latest of Omniverse foreign [Music] [Music] [Music] [Music] foreign [Music] foreign [Music] [Music] [Music] foreign just make you happy no art all physics physics make you happy doesn't it physics makes you happy okay well we've put uh we wanted to put Omniverse Civil War you could download Omniverse from our website and run it on your PC and your workstation for enthusiasts and and designers that's perfect you could also license Omniverse for Enterprise and for Enterprises that are that are using it across many different organizations we even set up a managed service for you inside your company we're putting Omniverse now in the cloud so that we could host and serve up apis that can be connected to developers and applications and services so that you can have the benefit of some of these amazing capabilities and so we're setting up Omniverse Cloud now Omniverse as I mentioned before is not a tool it's a platform for tools it's not a tool it's a platform for tools and we created a whole bunch of interesting tools to help you get started the reference applications and many of them are open sourced one application for for example that I really love is Isaac Sim it's a gym for teaching robots for robots to learn how to be a robot and because it's so physically accurate the Sim to real Gap is reduced and so theoretically you should be able to learn as a robot how to be a robot inside Omniverse and that neural network that software could then be put into a local embedded device like a Jetson or one of nvidia's Jetson computers robotic computers and the robot can perform its task okay and so we would like to put Omniverse in as many places as possible there whole bunch of different applications and this siggraph we're announcing a few new apis really cool apis and this one API this is the Run USD API of course this should be the first API what do you guys think pretty cute huh and so you send to the cloud USD and what comes out of the cloud what streams from the cloud onto your device in openxr or reality kit to your spatial Computing device will be this incredibly beautifully rendered and very importantly interactive USD so let's take a look at it [Music] [Music] [Applause] so for USD programmers USD developers you will have hours of Joy just hours and hours of joy and you can just create your USD content USD asset you know load it up on nvidia's Omniverse cloud and enjoy the device uh enjoy the the asset on your device now there's another API that we're creating and we showed you earlier how we used AI workbench to train uh this model to fine-tune this model we started with llama2 and we taught it we taught it we fine-tuned it for USD and so let's take a look at the video for USD developers building profiling and optimizing large 3D scenes can be a very complex process chat USD is an llm that's fine-tuned with USD functions and python UST code Snippets using Nvidia AI workbench and the Nemo framework this generative AI copilot is easily accessed as an Omniverse Cloud API simplifying your USD development tasks directly in Omniverse use chat USD for general knowledge like to understand the geometry properties of your USD schema or complete previously tedious repetitive tasks like generating code to find and replace materials on specific objects or to instantly expose all variants of a USD print [Music] chat USD can also help you build complex scenes such as scaling a scene and organizing it in a certain way in your USD stage [Music] built bigger more complex Virtual Worlds faster than ever with chat USB generative AI for USD workflows USD [Applause] chat USD now everybody can speak USD and try USB USD could be a USD teacher it could be a USD copilot and help you create your virtual world okay enhance your productivity incredibly and that's this is going to be also available on the Omniverse Cloud unfortunately their industry has to be physically coherent it's physically based they build things they build and operate physical things but they would love to do it digitally just like us and so how can we help them do that well this is where Omniverse and artificial and generative AI comes together for us to be able to help the heavy industries of the world digitalize their workflow just as modeling and texturing and lighting and you know animation and set design and so on and so forth are all done by different groups in a very complicated pipeline this is very much the case of the world's heavy Industries every one of their organizations from design to styling to engineering and simulation and testing to factory building and design Factory planning to build the products and even operating these software-defined robotics assisted products in the future all of this is done completely mechanically today that entire flow could be digitalized and it could be integrated for the very first time using open USD this is the incredible vision of openusd why we're so excited about it if we can only augment it with real-time capability and physics simulation capability and make it so that every single tool is connected to Omniverse we can digitalize the world's Industries well their excitement is enormous because they all would love to have the productivity we'd love to reduce the energy consumed we'd love to reduce the waste we'd love to reduce the mistakes in digital long before they have to build it in physical and so this is Mercedes they're using Omniverse to digitalize their manufacturing lines this is Mercedes using Omniverse to simulate autonomous vehicles this is BMW using Omniverse to digitalize their Global Network of factories some 30 factories they're now building in an Omniverse without breaking ground and doing the entire integration a year before the factory is actually even built using Omniverse to simulate new electric vehicle production lines remember the placement of the factory the planning of the factory and the programming of all the robotic systems are just incredibly complicated in the future entire factories will be software defined the factory will be robotic orchestrating a whole bunch of robots that are building cars that themselves are robotic so robots building robot orchestrating robots building robots so that's the future and everything is all software driven everything is all AI enabled this is BMW using AI to drive their operations this is wistron using Omniverse to digitalize their production line to build this machine as I mentioned 35 000 Parts incredibly complicated one of the most expensive ones the most valuable instruments that's made anywhere and so having that line be completely robotic and automated is really important this is pegatron using Omniverse to digitalize PCB manufacturing again this is the PCB of this incredibly complicated PCB motherboard this is the most complex motherboard the world's ever made techman is using Omniverse to test and simulate cobots it's not surprising to you but most cobots most robots today are not very autonomous not very AI driven programming the robots usually cost way more than robots themselves I heard it's a statistic that the robot for a manufacturing arm for the automotive industry is something along the lines of twenty five thousand dollars not very much but programming it could cost a quarter million dollars which is very sensible we would like to have ai be self-programming these autonomous autonomous limbs so technical is using Omniverse to test and simulate cobots they're also using Omniverse to build applications to automate Optical inspection of course for PCB lines the cameras can be stationary the the product the manufacturing device system is just rolling by you but for many things like cars and other very complicated systems the optical inspection has to follow the curvature and of course the various Contours and shapes of the product hexagon is using Omniverse to connect just as we connect the tools all over the world hexagon is using Omniverse to connect their own tools this is one of the most powerful things that that they observed in Omniverse whereas they had different groups and different teams are in silos and because they're incompatible tools getting them to connect together was hard and so this was a company organization and a company management challenge so very first time using Omniverse they broke down the silos they connected all the different teams and they became one unified company for the very first time really really cool this is ready robotics using Omniverse to build applications to simplify the robot programming process in the future robot programming is probably going to be about explaining in prompts what you would like the robot to do showing in a few examples and just as we taught our language model our generative model toy Jensen just as we taught the generative model USD we're going to teach a generative model of the future of robotic generative Model A few examples and it will be able to generalize and do that task Amazon is using Omniverse to digitalize their warehouse the warehouse is robotic giant system help cut help their their workers inside not have to work us work us walk as far Amazon is using Omniverse to simulate their Fleet of amrs these are autonomous moving robots and using Omniverse to generate synthetic data to train the perception models the computer vision models of these robots you can also use on reverse to create a digital twin Nvidia is creating a digital twin of the Earth the climate system of the earth Deutsche mine is using Omniverse to create a digital twin of their entire Railway Network so they could operate it completely in digital now in order for that to happen Omniverse has to be real time well let me show you one more example and this one example uh is about uh human human designers Architects working side by side with generative AI models from different applications in different companies and together they will automate and try to and and help do industrial digitalization a lot more rapidly okay roll it planning industrial spaces like factories or warehouses is a long complex process let's see how you can use Nvidia Omniverse and generative AI to connect your open USD to Fast Track planning Concepts like a storage extension to an existing Factory use sync twins Omniverse extension to quickly convert a 2d CAD floor plan into a 3D open USD model [Music] and populate it with Sim ready open USD assets using omniverse's AI enabled deep search then use prompts to generate physically accurate lighting options with blender GPT realistic floor materials with Adobe Fireflight and an hdri Sky Dome with blockade labs [Music] to see the new space and context compose it on a czm geospatial plane next to your existing Factory digital twin then to share with stakeholders use one click to publish the proposal to Omniverse Cloud gdn which serves a fully interactive review experience to any device fast track your factory planning process with Nvidia Omniverse and generative AI incredible is that they remember it started with a 2d PowerPoint slide and it ended with a virtual Factory in spatial computing that is incredible PowerPoint to a virtual Factory we hope and this is the beginning of a journey that we will finally be able to digitalize to bring software-driven artificial intelligence powered workflows into the world's heavy Industries the 50 trillion dollars worth of industries that are wasting enormous amounts of energy and money and time all the time because it was simply based built on technology that wasn't available at the time and so Omniverse for industrial visualization well all of this momentum that we've already seen with openusd is about to get turbocharged Alliance for open USD was announced with Pixar Apple Adobe Autodesk Nvidia as the founding members the alliance's mission is to Foster development and standardization of open USD and accelerate this adoption so whatever momentum we've already enjoyed the vision that we've already enjoyed it's about get kicked into Turbo Charge well I want to thank all of you for coming today we talked about the processor we created for this era of accelerated Computing and generative AI is Grace Hopper and we call it gh200 we have Nvidia AI workbench to make it possible for all of you to be able to engage onverse now has a major release with generative Ai and of course release and support for open USD and then finally whether you want to compute in the cloud or do AI in your company underneath your desk or in your data center we now have incredibly powerful systems to help you all do that remember accelerated Computing and generative AI the more you buy the more you save foreign

Channel: Explained Briefly

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Keywords: NVIDIA Keynote At Siggraph 2023, NVIDIA GPU, NVIDIA A100 GPU, NVIDIA Generative AI, NVIDIA Generative AI GPU, NVIDIA Avatar Cloud Engine, ChatGPT NVIDIA, Nividia Accelerated Computing, nvidia open ai, generative ai, artificial intelligence, NVIDIA, Siggraph 2023, Nvidia GH200, Nvidia Grace Hopper, NVIDIA H100 GPU, siggraph 2023, ai, nvidia gh200, nvidia ai h100 gpu, nvidia omniverse, nvidia omniverse open source, Nvidia OpenUSD, nvidia gh200 grace hopper, NVIDIA stock

Id: K4-SUsFFusE

Length: 52min 37sec (3157 seconds)

Published: Wed Aug 09 2023