NVIDIA Unveils "NIMS" Digital Humans, Robots, Earth 2.0, and AI Factories

Nvidia's Vision for the Future of AI

Key Announcements by Jensen Huang

• Jensen Huang, CEO of Nvidia, presents groundbreaking announcements regarding the future of artificial intelligence, including concepts like AI factories and digital humans.

• Huang emphasizes that all demonstrations in his keynote are simulations created with AI, showcasing innovations from weather prediction to automated factory robots.

The Power of CUDA Framework

• Huang explains that everything presented is based on simulation and computer science, highlighting Nvidia's Omniverse as a virtual world for these technologies.

• He discusses the significance of the CUDA framework, which serves as a critical library for running GPUs and performing complex calculations at scale.

Challenges in Accelerated Computing

• Despite its cost-saving potential, accelerated computing remains underutilized due to its complexity; rewriting software is necessary to achieve significant performance improvements.

• Huang stresses that transitioning applications from CPU-based algorithms to GPU acceleration requires extensive reworking of existing software.

Innovations in Libraries and Applications

• Nvidia has developed various libraries over 20 years to simplify access to accelerated computing, including cuDNN for deep learning and a new library for AI physics applications.

• The introduction of Aerial allows for software-defined telecommunications networks, paralleling advancements made in internet networking.

Advanced Computational Solutions

• Litho is highlighted as a computational lithography platform aiding chip manufacturing processes while saving energy and costs.

• Parab Bres offers high-throughput gene sequencing capabilities, while Coop addresses complex optimization problems traditionally thought solvable only by quantum computers.

Quantum Computing Emulation

• C Quantum serves as an emulator for quantum computers used by researchers globally; it integrates into leading frameworks essential for developing quantum algorithms.

• CDF accelerates data processing libraries crucial for cloud computing efficiency; this includes popular tools like Spark and Pandas.

Ecosystem Development through Libraries

• Nvidia’s creation of domain-specific libraries enables broader access to accelerated computing across various fields; they have developed around 350 such libraries.

• These libraries facilitate deep learning scientists' work by bridging gaps between CUDA and popular machine learning frameworks like TensorFlow and PyTorch.

Earth 2: A Digital Twin Concept

Digital Twin of Earth: A New Era in Predictive Modeling

The Concept of a Digital Twin

• The idea is to create a digital twin of Earth to simulate and predict future scenarios, helping us avert disasters and understand climate change impacts.

• This project is described as one of the most ambitious global undertakings, with significant progress made annually.

Breakthroughs in Weather Prediction

• Recent advancements include improved predictions for storms, such as their paths and potential impacts on regions like Taiwan.

The Big Bang of AI: Transforming Software Development

Generative AI Revolution

• The speaker refers to the emergence of generative AI as a transformative moment, likening it to a "Big Bang" that reshapes our understanding of AI capabilities.

• Unlike traditional software delivery methods (e.g., packaged software), the focus has shifted towards delivering AI models that can autonomously generate software.

Evolution from Perception to Generation

• Prior to ChatGPT, AI was primarily about perception tasks; now it encompasses generative capabilities that produce various forms of content (text, images, etc.).

• Tokens generated by AI can represent diverse data types including weather patterns or even complex scientific concepts like physics.

AI as an Industrial Revolution

New Commodities and Market Opportunities

• The evolution from supercomputers to data centers signifies the birth of an "AI Factory," producing valuable tokens akin to how AC generators produced electrons in the past.

• This new commodity generation presents vast market opportunities across multiple industries.

Impact on Computing Models

• The IT industry is poised for transformation, moving from traditional computing roles into generating intelligence for various sectors.

Future Computing Paradigms

Shift in Data Processing Approaches

• A significant shift occurs where all layers of computing are being redefined; moving from CPU-based systems to accelerated GPU computing focused on large language models (LLMs).

Generative vs. Retrieval-Based Systems

• Future computers will prioritize generating data over retrieving pre-existing information, leading to more energy-efficient processes and contextually relevant outputs.

Nvidia's Inference Microservices

Introduction of Nims

Understanding the Complexity of AI and Digital Humans

The Complexity of AI Systems

• The Nim operates within a complex factory-like environment, representing a pre-trained AI model that relies on an intricate computing stack.

• Large-scale models, such as those used in ChatGPT, consist of billions to trillions of parameters and require multiple computers for processing through various forms of parallelism.

• Throughput in data centers is crucial; it directly impacts revenue, service quality, and user accessibility.

• Modern companies meticulously measure every operational parameter (e.g., start time, uptime, throughput), emphasizing the factory analogy for AI operations.

• NVIDIA has developed an "AI in a box" solution that integrates numerous software components to simplify access to advanced AI capabilities.

Features of the Nim Model

• The Nim includes essential tools like CUDA, CNN TensorRT, and Triton for inference services while being cloud-native for scalability in Kubernetes environments.

• Users can interact with the Nim via standard APIs after downloading it; this integration allows seamless communication similar to ChatGPT.

• Extensive testing was conducted across various hardware versions (Pascal, Ampere, Hopper), ensuring compatibility and performance optimization.

• The availability of diverse pre-trained models caters to different applications including language processing and healthcare solutions.

• Users can access Llama 3 on Hugging Face for free; it can be run in the cloud or hosted locally.

Innovations in Digital Human Technology

• Digital humans are entirely AI-generated entities capable of real-time interaction without pre-rendering animations or graphics.

• These digital humans serve as interactive agents that enhance engagement through human-like interactions using text and speech prompts.

• Achieving realism is critical; overcoming the "uncanny valley" effect is necessary for effective human-computer interaction with digital humans.

• Digital humans have potential applications across industries such as customer service, advertising, gaming, interior design assistance, and personalized healthcare support.

Digital Humans and AI Architecture Evolution

Foundations of Digital Humans

• Digital humans are created using AI models that leverage multilingual speech recognition, synthesis, and large language models (LLMs) to understand and generate conversation.

• These models enable realistic 3D facial animations through generative AI, which dynamically animates lifelike appearances with advanced light simulation techniques.

• Nvidia's Ace suite offers digital human technologies as microservices, allowing developers to integrate them into existing frameworks for enhanced user experiences.

Advancements in AI Infrastructure

• The discussion shifts towards the evolution of AI architecture, emphasizing the rapid growth in hardware capabilities and its implications for future developments.

• Scaling data centers has led to significant advancements; each scale introduces new phases of capability enhancement, particularly in training large datasets with Transformers.

Transition from Supervised to Unsupervised Learning

• Initially reliant on human labeling for training data, the emergence of Transformers allows for unsupervised learning by analyzing vast amounts of unlabelled data.

• Future AI systems must be grounded in physical laws to effectively generate images and videos; this requires a shift towards physically-based learning methods.

Synthetic Data Generation Techniques

• Learning from video content is one method for grounding AI in reality; synthetic data generation through simulations is another promising approach.

• Reinforcement learning combined with self-play can enhance AI intelligence by enabling models to learn from interactions over extended periods.

Introduction of Blackwell GPU Architecture

• The presentation introduces Blackwell as Nvidia's new GPU architecture designed to support larger model requirements and improve computational efficiency.

• Key features include a high-speed connection between two large chips, enhancing processing power while maintaining low latency across applications.

Enhancements in Reliability and Performance

• The second-generation Transformer engine adapts precision dynamically based on computation needs, improving overall performance during inference tasks.

Blackwell: A Leap in Computing Technology

Introduction to Blackwell

• Blackwell is presented as a revolutionary computing technology, capable of data storage and processing 20 times faster than current capabilities.

• The speaker introduces the production version of Blackwell, highlighting its complexity and performance as the most advanced computer ever created.

Technical Specifications

• Blackwell features a gray CPU with two interconnected large dies, utilizing a 10 terabits per second link for enhanced performance.

• The computational power has significantly increased over eight years, surpassing Moore's Law, which has slowed down during this period.

Energy Efficiency Improvements

• Training energy consumption for models like GPT-4 has decreased by 350 times due to advancements in computational capability.

• Previously unfeasible energy requirements for large language models have been drastically reduced; what once required 1,000 gigawatt hours now only needs about 3 gigawatt hours.

Token Generation Performance

• Token generation efficiency has improved dramatically; it now takes only 0.4 joules per token compared to Pascal's requirement of 17,000 joules.

• This reduction allows for rapid token generation at minimal energy costs, making advanced AI applications more feasible.

System Architecture and Cooling Solutions

• The architecture includes DGX systems that house multiple Blackwell chips; air-cooled versions are highlighted alongside liquid cooling options.

• The MGX modular system can accommodate up to 72 GPUs connected via an advanced MVLink switch, enhancing bandwidth and performance significantly.

Advanced Connectivity Features

• The MVLink switch connects all GPUs within the system efficiently; it boasts impressive specifications including high transistor counts and substantial bandwidth capabilities.

Nvidia's GPU Evolution and Networking Innovations

The Transformation of GPUs

• Nvidia has significantly evolved the design and functionality of GPUs, showcasing both consumer-grade and advanced models. The speaker highlights a gamer GPU as well as a more complex model referred to as "DGX."

Understanding the MV Link Spine

• The MV link spine consists of 5,000 wires spanning two miles, connecting 702 GPUs. This intricate setup is described as an "electrical mechanical miracle," emphasizing its engineering complexity.

Energy Efficiency in Data Centers

• Utilizing the MV link spine allows for energy savings of up to 20 kilowatts per rack, which can be redirected towards processing power—an impressive achievement in data center efficiency.

Networking Challenges in AI Factories

• Two primary networking types are discussed: InfiniBand (used in supercomputing) and Ethernet. While InfiniBand is growing rapidly, many data centers remain committed to Ethernet due to prior investments.

Bridging InfiniBand with Ethernet

• Nvidia aims to integrate InfiniBand capabilities into Ethernet architecture, addressing challenges such as managing high average throughput versus bursty traffic typical in AI workloads.

Communication Dynamics Among GPUs

• In deep learning environments, GPUs primarily communicate with each other rather than external users. This internal communication involves collecting and redistributing partial products efficiently.

Addressing Throughput Limitations

• The focus shifts from average throughput to last arrival timing; the last response received is crucial for efficient processing within AI tasks—a challenge not addressed by traditional Ethernet designs.

Innovative Solutions for Network Architecture

• Nvidia introduces four key technologies:

• Advanced RDMA capabilities for Ethernet.

• Real-time congestion control through telemetry.

• Adaptive routing that ensures packets are sent via available ports while maintaining order.

• Noise isolation techniques to prevent interference between multiple training models.

Cost Implications of Network Utilization

• Inefficient network utilization can inflate operational costs significantly; a $5 billion data center could effectively operate like a $6 billion facility if performance drops by just 40%.

Spectrum X: Future-Proofing Data Centers

• Nvidia's Spectrum X800 product offers substantial bandwidth (51.2 terabits per second), designed for scaling from tens of thousands to millions of GPUs—anticipating future demands driven by generative AI applications.

Generative AI Integration

• As generative AI becomes ubiquitous across interactions with computers and the internet, it will require robust infrastructure capable of supporting extensive computational needs—both on-premises and cloud-based solutions.

Conclusion: A New Era in Keynote Presentations

Nvidia's Blackwell Platform and Future Innovations

Introduction to Blackwell

• The Blackwell platform marks the beginning of a new era in generative AI, coinciding with the rise of AI factories and an Industrial Revolution.

• There is widespread support for Blackwell from various sectors including OEMs, computer makers, CSPs, and telecommunication companies.

Performance and Cost Efficiency

• Nvidia aims to enhance performance while reducing training and inference costs, making AI capabilities more accessible for companies.

• The Hopper platform has been highly successful in data center processing history; however, Blackwell is positioned as the next significant advancement.

Modular System Design

• Each generation of Nvidia platforms integrates multiple components (CPU, GPU, MVLink, NIC, switches), creating a comprehensive AI Factory supercomputer.

• The design philosophy emphasizes modularity by disaggregating systems to allow customization for different data centers and customer needs.

Technological Advancements

• Nvidia follows a one-year rhythm for product development while pushing technology limits across various domains such as packaging and memory.

• Software compatibility is crucial; maintaining architectural compatibility ensures faster market readiness leveraging existing software infrastructure.

Future Developments

• Upcoming innovations include the Blackwell Ultra platform expected next year alongside advancements in Spectrum switches.

• The next-generation platform is codenamed "Reuben," with full development underway on all chips within this cycle.

The Evolution of Nvidia: From ImagNet to Today

Historical Context

• Over the past 12 years since ImagNet's impact on computing futures, Nvidia has undergone significant transformation in its offerings.

Transitioning to Physical AI

• Following discussions on Blackwell, Dr. Jim Fan will introduce concepts related to physical AI—AI that comprehends physics laws and interacts effectively with humans.

Robotics Integration

• Future robotics will not be limited to humanoid forms but will encompass robotic systems throughout factories orchestrating production processes.

The Era of Robotics: Advancements in Physical AI

Introduction to Physical AI

• The era of robotics is upon us, with a vision that everything capable of movement will become autonomous.

• Researchers and companies globally are developing robots powered by physical AI, which can understand instructions and perform complex tasks autonomously.

Learning Mechanisms for Robots

• Robots now utilize human demonstrations to learn necessary skills for interacting with their environment, employing both gross and fine motor skills.

• Generative physical AI leverages reinforcement learning from physics feedback within simulated environments, allowing robots to make decisions based on actions performed in a virtual world.

Simulation Environments and Training

• Nvidia Omniverse serves as the operating system for creating physical AIs, providing a platform for virtual world simulation that combines real-time rendering and generative AI technologies.

• In Omniverse, robots learn to manipulate objects precisely and navigate environments while avoiding obstacles, effectively minimizing the gap between simulation and real-world application.

Infrastructure Requirements

• Building robots with generative physical AI necessitates three types of computers: Nvidia AI supercomputers for training models, Jetson Orin for running models, and Omniverse for skill refinement in simulations.

• Developers are provided with platforms, libraries, and models tailored to their needs as they prepare for the next wave of robotics powered by physical AI.

Digital Twins in Factory Automation

Overview of Digital Twin Technology

• Factories operate within distinct ecosystems where advanced robotics software integrates various components like edge computers and PLC systems.

• Companies such as Foxconn are building digital twins—virtual replicas of factories—to optimize workflows through integration with leading industry applications.

Benefits of Digital Twins

• The demand for Nvidia's accelerated computing is increasing as traditional data centers transition into generative AI factories.

• Digital twins allow planners to visualize equipment layouts accurately before construction begins, significantly reducing costs associated with physical changes during development.

Robotic Training in Simulated Environments

• Foxconn utilizes the Omniverse digital twin not only as a planning tool but also as a robot gym where developers train robotic perception applications.

• Simulations include testing automated optical inspection systems that enhance object identification capabilities before deploying them on assembly lines.

Integration Across Systems

• Robotic factories designed by Foxconn incorporate multiple computer systems working together seamlessly within shared virtual spaces.

AI Robotics Innovations and Future Prospects

Integration of AI in Robotics

• Isaac manipulator and perceptor are being integrated into AI robots to enhance manufacturing efficiencies for global customers, particularly in factory logistics.

• Companies like Gideon and Argo Robotics are adopting Isaac's technology for advanced logistics solutions, including AI-powered forklifts and perception engines for vision-based AMRs (Autonomous Mobile Robots).

• Various robotics firms are integrating Isaac manipulator into their systems, such as TM Flow for automated optical inspection and Polycope X for cobots.

Advancements in Robotic Capabilities

• The current landscape of robotics is not science fiction; it is actively being implemented across Taiwan with significant advancements in self-driving cars featuring autonomous capabilities.

• Nvidia plans to go into production with the Mercedes fleet next year, followed by the JLR fleet in 2026, offering a full stack of robotic technologies that can be utilized selectively.

Future Trends in Robotics

• Humanoid robots represent a promising area due to recent progress in cognitive capabilities and world understanding through foundation models.

• The adaptability of humanoid robots is enhanced by the vast amount of training data available from human-like interactions, which facilitates better demonstration capabilities.

The Role of Technology in Robotics Development

• The future of robotics involves creating computers that can walk or roll, emphasizing that these machines share technological similarities with existing computer systems.