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GTC March 2024 Keynote with NVIDIA CEO Jensen Huang

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💫 Short Summary

The video showcases the diverse applications of AI, from assisting the blind to renewable energy and healthcare. Nvidia's AI technology is highlighted, emphasizing its role in driving down costs and accelerating computing. The video discusses advancements in creating giant GPUs and the development of a supercomputer. It delves into the importance of encryption, data movement, and generative AI in computing. Nvidia's collaboration with industry leaders and the transformative power of AI in manufacturing, healthcare, and weather forecasting are also discussed. The future of software development involves AI interfaces, chatbots, and robotics, with a focus on advancing AI models and generative AI capabilities.

✨ Highlights
📊 Transcript
The diverse applications of AI showcased in the video segment.
AI is used to assist the blind, generate virtual scenarios, and improve renewable energy, robotics, and healthcare.
Nvidia's AI technology and the CEO's address at a developers conference highlight the importance of AI in climate science and self-driving cars.
Industry leaders like Michael Dell emphasize the significance of AI in shaping the future of society.
Accelerated computing is being used in various industries to solve problems traditional computers cannot address.
Industries such as life sciences, healthcare, genomics, transportation, retail, logistics, and manufacturing are benefiting from accelerated computing.
Computing has had a transformative impact on industries, showcasing significant progress in the field.
Evolution of computing models like CUDA and advanced AI supercomputers demonstrate continuous innovation and growth.
The emergence of generative AI indicates the start of a new industry focused on creating previously non-existent software, marking a significant shift in technology development.
Highlights of Nvidia's discussion on accelerated computing.
Nvidia emphasizes the importance of accelerated computing for reducing costs, increasing consumption, and ensuring sustainability.
Accelerated computing is significantly faster than general-purpose computing, with major impacts on various industries.
Simulation tools for product creation are a key application area for accelerated computing.
NVIDIA focusing on driving down the cost of computing and increasing the scale of digital twins for product design, simulation, and operation.
Partnerships announced with Ansys and Cadence to enhance computational lithography and generative AI in semiconductor manufacturing.
NVIDIA working towards creating a supercomputer using GPUs for fluid dynamic simulations on a large scale.
Use of Nvidia GPUs in software companies for building supercomputers and applications like co-piloting and connecting digital twin platforms to Omniverse.
Large language models are scaling, leading to exponential growth in computational requirements due to parameter count doubling.
Significantly larger GPUs are needed to support training models with trillions of parameters, requiring billions of floating-point operations per second.
Emphasis on innovation and collaboration in GPU technology development to address future computational challenges.
Advancements in creating giant GPUs and connecting them using networks for supercomputers.
Development of the DJX1 and construction of one of the largest AI supercomputers by 2023.
Focus on building chips, systems, and networks to distribute computation efficiently across thousands of GPUs.
Emphasis on the need for larger models trained on multimodality data for future innovations.
Grounding AI models in physics through watching videos and languages.
Introduction of the Blackwell chip as the most advanced GPU with 208 billion transistors.
The chip is comprised of two integrated dies functioning as one giant chip, offering 10 terabytes of data transfer per second.
The Blackwell chip is designed to be compatible with the Hopper system, enabling efficient global installation.
Despite initial skepticism from engineers, the Blackwell chip impressed with its ambitious goals, innovative design, and capabilities.
Introduction to new hopper version for hgx configuration.
Prototype board features advanced design with high computation power and memory coherence.
MV link and PCI Express included with CPU chip-to-chip links.
Second generation Transformer engine introduced for dynamically rescaling numerical formats.
Engine essential for artificial intelligence and complex mathematical calculations.
Highlights of the new Transformer engine features.
The new Transformer engine is twice as fast as Hopper and has computation in the network to allow GPUs to work together efficiently.
All reduce, all to all, and all gather are key synchronization methods used in the new Transformer engine.
The reliability engine in the Ras chip ensures thorough self-testing of every component, maximizing supercomputer utilization.
Secure AI is prioritized through data encryption in transit and at rest to safeguard AI parameters from loss or contamination.
Importance of encryption, transmission, compression, and decompression in computing.
Fast data movement in and out of computers is essential to prevent them from being idle.
Advancements in AI computing, particularly generative AI and understanding context for efficient information retrieval and production.
Future of computing is seen as generative AI, leading to energy, bandwidth, and time savings.
Shift in computing represents a new industry with fundamentally different computational approaches.
Advancements in computing technology have rapidly increased, focusing on content token generation and inference capability.
Computation has increased by 1,000 times in the past eight years, demonstrating exponential growth in technology.
The Envy link switch chip, with 50 billion transistors, enables every GPU to communicate at full speed simultaneously.
The chip's groundbreaking innovation allows for driving copper directly, connecting GPUs effectively as one giant unit.
This development signifies a significant step towards more powerful and cost-effective computing systems.
Discussion of a powerful AI system with 720 pedop flops, one of the world's first exaflops machines.
The system is liquid-cooled, consuming 120 kilowatts and operating at 45°C.
Features a unique MVLink spine with 130 terabytes per second bandwidth, equivalent to the entire internet's aggregate bandwidth.
Evolution of GPUs, from 35,000 parts to 600,000 parts, weighing 3,000 pounds like a Carbon Fiber Ferrari.
Emphasis on significant advancements in technology and computational power.
Computational requirements for training large language models like GPT are discussed.
Significant resources needed include 8,000 GPUs and 15 megawatts of power for training.
The goal is to reduce costs and energy consumption associated with computing to enable scaling up model training.
Inference for large language models poses challenges due to their size exceeding the capacity of a single GPU.
The shift towards complex inference tasks like chatbots and generating content necessitates supercomputing resources, marking a change in computing demands.
Advancements in chatbot technology allow for the generation of tokens at interactive rates using trillions of tokens and parameters.
Effective communication with chatbots involves selecting appropriate analogies.
Quick token generation is vital, necessitating the parallelization of models across multiple GPUs.
Balancing throughput and interactive rate impacts the cost and quality of service delivery.
Optimizing work distribution across GPUs is crucial for achieving high throughput and interactivity.
NVIDIA's GPUs enable extensive exploration in performance and software configuration.
Blackwell's inference capability for generative AI surpasses Hopper by 30 times, with potential for large language models like GPT.
Improvements in chip size and speed, including FP4 tensor core and MV link switch, enhance communication speed and efficiency among GPUs.
Data centers are envisioned as AI Factories generating revenue through AI applications.
Blackwell, a revolutionary product in the tech industry, is set to launch with global partners AWS, Google, and Nvidia.
The product boasts advanced GPU systems and collaborations with various companies for accelerated computing and AI development.
Blackwell's impact extends to infrastructure development, robotics, and healthcare integration.
The product promises to be the most successful launch in history, showcasing its potential to revolutionize multiple sectors.
Collaboration between Google, GCP, Oracle, and Nvidia to accelerate services and databases, particularly focusing on digital twin technology.
Wistron, a manufacturing partner, is building digital twins of Nvidia factories using custom software and Omniverse sdks.
Digital twins help optimize layouts, increase worker efficiency, and ensure physical builds match digital plans, reducing costs and improving operations.
Wistron's factory was brought online in half the time using digital twins, enabling rapid testing of layouts and real-time monitoring of operations.
This resulted in significant efficiency gains for Wistron.
Nvidia's global ecosystem of partners driving accelerated AI-enabled digitalization.
AI transforming manufacturing with digital product creation before physical manufacturing.
Advances in AI technology, like compressing dimensions for efficient processing.
Evolution of AI from recognizing to understanding text and images for tasks like chatting and summarizing.
Advancements in generative AI have enabled the digitization and analysis of proteins, genes, brain waves, and weather patterns.
Earth 2.0, a digital twin of Earth, allows for the prediction of extreme weather events with high resolution and accuracy.
Nvidia's Cordi AI model has transformed weather forecasting by improving resolution from 25 km to 2 km and increasing speed and energy efficiency.
The technology can offer detailed regional weather forecasts, helping to mitigate potential damages and loss of life from severe storms.
Nvidia and The Weather Company collaborating to enhance global weather predictions and integrating Earth-2-Cordi technology.
Nvidia advancing in healthcare with AI models for medical imaging, gene sequencing, and computational chemistry.
The AlphaFold project digitizing and reconstructing 200 million proteins, revolutionizing protein structure prediction.
Nvidia's new generative screening paradigm using Nemo Nims, AlphaFold, and diff dock technologies for rapid identification of new drug candidates through virtual screening for new medicines.
Nvidia MIM can optimize molecule properties for drug discovery through custom applications.
Nims offer OnDemand microservices for drug discovery workflows like denovo protein design.
Nvidia Inference Microservice (Nim) is a pre-trained model with state-of-the-art open source models and user-friendly APIs.
Nim is optimized for single GPU, multi-GPU, or multi-node setups.
Nim is designed for easy integration and use in AI applications.
The future of software development involves using AI interfaces like 'Nims' for seamless communication and task handling.
These specialized Nims can collaborate in different areas to provide efficient solutions and automate processes for increased productivity and innovation.
Nvidia has integrated Nims into their organization to create chatbots and streamline operations, showcasing the benefits of this concept in modern software development.
The use of Nims allows for easy scalability and optimization, making them a valuable asset for companies looking to enhance their software development capabilities.
Nvidia utilizes chatbots for chip designing, specifically with the creation of a chatbot named Llama 2.
Initially, the chatbot misunderstood the term CTL as combinatorial timing logic but was later trained to identify it as Compute Trace Library.
Nvidia offers a service called Nemo microservice for customizing and fine-tuning AI models, along with infrastructure like dgx cloud for deployment.
The goal of Nvidia is to become an AI Foundry similar to TSMC in chip manufacturing, providing tools and technology for AI development.
Creation of a Vector Database for Company Data.
Company data is primarily stored internally and the goal is to extract meaning from it by creating a vector database.
The vector database encodes structured and unstructured data into vectors to facilitate communication with the database.
Nemo Retriever is a service designed to quickly retrieve information from the vector database upon request.
Digital Nims, including a digital human named Rachel, serve different purposes such as being an AI care manager connected to a healthcare language model.
Collaboration with Leading Companies in the Enterprise IT Industry.
Nvidia AI Foundry works with companies like SAP, ServiceNow, and Cohesity to develop AI solutions.
Use of Nvidia Nemo and DGX Cloud for building co-pilots and chatbots.
Snowflake partners with Nvidia AI Foundry to store digital data in the cloud and create co-pilots.
Collaboration with NetApp and Dell to develop chatbots and co-pilots, highlighting Dell's expertise in building AI factories for large-scale enterprise systems.
Training AI involves inputting data to create large language models with trillions of parameters.
Three types of computers are needed to advance AI to understand the physical world.
AI computer watches videos and generates data, Jetson for autonomous processing, and another for language models.
Reinforcement learning involves providing human feedback to AI robots for physical alignment and learning articulation capabilities.
Use of simulation engines like Omniverse and OVX in robotics for learning and adaptation to physical laws.
Introduction of a warehouse scenario with autonomous systems interacting under central control.
Highlighting the concept of a digital twin for heavy industry to assist robots and workers in navigating complex environments.
Utilization of Omniverse digital twin of a 100,000 ft warehouse for evaluating and refining system adaptability to real-world unpredictability.
Use of generative AI powered Metropolis Vision Foundation models to improve mission efficiency.
Operators can ask questions using natural language and receive immediate insights to enhance operations.
Sensor data is created in simulation and processed in real-time by AI.
Integration of digital twins and AI models enables continuous improvement in virtual and physical environments.
Omniverse is being simplified with Cloud APIs for easier access and enhanced capabilities in digital twin communication and AI integration.
Siemens integrates Nvidia AI and Omniverse technologies into their Teamcenter platform.
Data interoperability, physics-based rendering, and generative AI streamline design and manufacturing processes.
HD and Hyundai benefit from unifying massive engineering data sets interactively, saving time and costs.
Collaboration with Nvidia accelerates computing, generative AI, and Omniverse integration in Siemens' accelerator portfolio.
Success seen with Nissan's workflow integration demonstrates the benefits of a unified approach.
NVIDIA introduces Omniverse Cloud streams to Vision Pro for virtual design tools integration.
Emphasis on robotics, particularly in the automotive industry for adoption of autonomous systems like self-driving cars.
NVIDIA's AV computer Thor is adopted by BYD, with over a million robotics developers.
Prioritization of compatibility for developers, offering a CUDA-compatible platform for software development.
Introduction of Isaac Perceptor SDK for robot perception and navigation.
Emphasis on the significance of perception in robots for adaptive route planning and environment adaptation.
Features of the technology include advanced vision, odometry, and 3D reconstruction capabilities.
Introduction of Isaac Manipulator with CUDA-accelerated motion planning and perception algorithms for 3D object pose estimation.
Mention of the potential of humanoid robotics in the future with available technology and imitation training data.
Development of humanoid robots with a focus on training them to adapt to the physical world and interact with humans.
Introduction of Nvidia Project Groot as a foundation model using multimodal instructions and past interactions to guide robot actions.
Tools like Isaac Lab and Osmo are highlighted for training and simulation purposes.
Groot model aims to enable robots to learn from human demonstrations and perform everyday tasks by observing human movement.
Nvidia's technologies are crucial in understanding humans, training models, and deploying them to physical robots.
Introduction to Jetson Thor robotics chips and General Robotics 003 project.
Emphasis on advancements in computer graphics, physics, and artificial intelligence for next-gen robotics.
Mention of a new Industrial Revolution focused on accelerating data centers.
Emergence of generative AI creating valuable software.
Importance of distributing new types of software in an easy-to-use and portable manner for the evolution of computing technology.
Nvidia introduces Nims, AI technology, tools, and infrastructure to create proprietary applications and chatbots.
The future will be dominated by robotics in various industries like stadiums, warehouses, and factories, all requiring a digital twin platform called Omniverse.
Nvidia's vision for the future involves a different image of GPUs focused on software stacks and innovative processors.
The Blackwell system design is highlighted as a significant advancement in GPU technology.