NVIDIA has built an incredible moat around its business through its extensive ecosystem of acceleration libraries and frameworks. Some key points:

• NVIDIA has created over 350 domain-specific acceleration libraries that enable developers to take advantage of accelerated computing. These include libraries for deep learning (cuDNN), physics simulation (PhysX), computational lithography (Litho), gene sequencing (cuPASA), and more.

• These libraries are critical for making accelerated computing accessible to developers. Without them, the complexity of porting algorithms to run on GPUs would be immense.

• The separation between the low-level CUDA framework and the high-level domain-specific libraries is what has enabled the widespread adoption of accelerated computing. It's akin to the importance of OpenGL for computer graphics or SQL for data processing.

• NVIDIA's extensive library ecosystem has created a massive moat around its business. Developers are deeply invested in these libraries, making it extremely difficult for competitors to break into the market.

• The ability to continuously expand this library ecosystem and keep it optimized for the latest hardware is a key competitive advantage for NVIDIA. It allows them to stay ahead of the curve and maintain their dominance in accelerated computing.

In summary, NVIDIA's extensive acceleration library ecosystem is a huge strategic asset that has built an incredibly strong moat around its business. This moat will be very difficult for competitors to overcome in the foreseeable future.

The idea of creating a digital twin of the Earth, which NVIDIA calls "Earth 2", is one of the most ambitious projects the world has ever undertaken. The goal is to simulate the Earth in order to better predict the future of our planet, avert disasters, and understand the impact of climate change so we can adapt better.

NVIDIA has made significant breakthroughs in this area. They have developed advanced simulation capabilities that can accurately model weather patterns, climate, and other physical phenomena. The digital twin of the Earth is powered by AI models that learn from vast amounts of data, allowing it to generate highly realistic simulations.

During the keynote, NVIDIA demonstrated how this digital twin can be used to predict the path and impact of an approaching storm in Taiwan. By running multiple simulations, the system was able to provide insights into the uncertainties around the storm's trajectory and potential effects on the region.

This technology represents a major step forward in our ability to understand and respond to the challenges facing our planet. By creating a comprehensive digital model of the Earth, NVIDIA is enabling scientists, policymakers, and others to explore the future in ways that were previously impossible. As the capabilities of this system continue to grow, it has the potential to transform how we approach global issues like climate change, natural disasters, and resource management.

Until ChatGPT revealed it to the world, AI was all about perception, natural language understanding, computer vision, and speech recognition. It was about detection and understanding.

However, ChatGPT introduced the world to generative AI - AI that can produce tokens, whether they be words, images, charts, tables, or even songs and videos. This represents a fundamental shift, as now AI can not only perceive and understand, but also generate new content.

This marks the beginning of a new era - the generative AI era. AI has now evolved from a supercomputer into a "Data Center" that produces a new commodity: tokens. Just as Nikola Tesla's AC generator produced electrons, Nvidia's AI generator produces tokens, which have large market opportunities across nearly every industry.

This represents a new Industrial Revolution. AI is no longer just an instrument for information storage or data processing, but a factory for generating intelligence for every industry. This shift from retrieval-based computing to generation-based computing will have a profound impact, as generated data requires less energy to fetch and is more contextually relevant.

To enable this new era, Nvidia has created Nvidia Inference Microservices (Nims) - pre-trained AI models packaged as easy-to-deploy, fully optimized microservices. This allows companies to quickly integrate generative AI capabilities into their applications and services.

The impact of this generative AI revolution will be far-reaching, as AI transitions from a tool we use to a generator of skills and capabilities. Just as the software industry revolutionized computing in the 1990s, the AI industry is now poised to revolutionize every industry it touches.

NVIDIA has created a suite of AI inference microservices, called NIMS, to make it easier for developers to integrate advanced AI capabilities into their applications. These NIMS are pre-trained AI models that can be easily deployed and used, without the complexity of building the underlying AI infrastructure.

The key aspects of NIMS include:

1. Pre-trained Models: NVIDIA has developed a variety of pre-trained AI models covering different domains like language, vision, robotics, and more. Developers can simply integrate these models into their applications without having to train the models from scratch.

2. Optimized for Performance: The NIMS are highly optimized to run efficiently on NVIDIA's GPU hardware, leveraging technologies like Tensor Cores and CUDA. This ensures low-latency and high-throughput inference performance.

3. Containerized Deployment: The NIMS are packaged as containers, making them easy to deploy in cloud, on-premises, or edge environments. Developers can simply pull the container image and run the AI model as a service.

4. Integrated Stack: The NIMS include the complete software stack required to run the AI models, including the NVIDIA runtime, inference engines, and other dependencies. This simplifies deployment and reduces the burden on developers.

5. Scalable and Reliable: The NIMS are designed to be scalable, with support for distributed inference across multiple GPUs. They also include features for high availability and fault tolerance to ensure reliable operation.

By providing these AI inference microservices, NVIDIA aims to democratize advanced AI capabilities and make it easier for developers to incorporate cutting-edge AI into their applications. This helps accelerate the adoption of AI across various industries and use cases.

Digital humans will revolutionize industry from customer service to advertising and gaming. The possibilities for digital humans are endless. Using advanced AI and computer graphics technologies, digital humans can see, understand, and interact with us in human-like ways.

The foundation of digital humans are AI models built on multilingual speech recognition and synthesis, and large language models that understand and generate conversation. These AI models connect to other generative AI to dynamically animate a lifelike 3D mesh of a face, and AI models that reproduce lifelike appearances enabling real-time path traced subsurface scattering to simulate the way light penetrates the skin, scatters, and exits at various points, giving skin its soft and translucent appearance.

Nvidia Ace is a suite of digital human technologies packaged as easy to deploy, fully optimized microservices or Nims. Developers can integrate Ace Nims into their existing frameworks, engines, and digital human experiences. These include Neotron SLM and LLM Nims to understand intent and orchestrate other models, Reva speech Nims for interactive speech and translation, and audio to face and gesture Nims for facial and body animation. Ace Nims run on Nvidia GDN, a global network of Nvidia accelerated infrastructure that delivers low latency digital human processing to over 100 regions.

Digital humans have the potential to be great interactive agents, making interactions much more engaging and empathetic. As the technology continues to advance, digital humans will see widespread adoption across industries, revolutionizing customer service, advertising, gaming, and beyond.

Jensen Huang discusses the rapid advancements in AI architecture and infrastructure, highlighting key milestones and future directions:

Scaling Data Centers and Transformers

• The scaling of Nvidia's data centers enabled the training of large Transformer models on massive datasets through unsupervised learning.
• This allowed AI models to learn patterns and relationships from data without the need for extensive human labeling.

Physically-Based AI

• The next generation of AI needs to be grounded in the physical world and understand the laws of physics.
• This can be achieved through learning from video, synthetic data simulation, and AI systems learning from interacting with each other.

Blackwell GPU Architecture

• Blackwell is Nvidia's new GPU architecture designed for the generative AI era.
• Key features include:
  • Largest chip ever made, with two chips connected at 10 TB/s
  • Second-generation Transformer engine for dynamic precision adaptation
  • Secure AI to protect models from theft or tampering
  • Fifth-generation NVLink for high-bandwidth GPU interconnect
  • Reliability and availability engine to enhance uptime
  • Decompression engine for faster data processing

Modular DGX and MGX Systems

• Blackwell chips are integrated into Nvidia's DGX and MGX modular systems.
• DGX systems provide air-cooled configurations, while MGX offers liquid-cooled options.
• These systems can be scaled up to connect hundreds of thousands of GPUs using Nvidia's advanced networking technologies.

Ethernet Innovations for AI Factories

• Nvidia has developed enhancements to Ethernet to make it suitable for the bursty, low-latency communication patterns required in AI training.
• Technologies like RDMA, congestion control, adaptive routing, and noise isolation enable Ethernet to perform on par with specialized InfiniBand networks.

Roadmap: Blackwell Ultra and Reuben Platforms

• Nvidia plans to continue its one-year cadence of pushing the limits of technology with the Blackwell Ultra and Reuben platforms.
• These future generations will maintain architectural compatibility to leverage the growing software ecosystem.

The key message is Nvidia's relentless pursuit of advancing AI architecture and infrastructure to enable the next wave of generative AI and physical AI applications.

Blackwell is NVIDIA's new GPU architecture, designed to power the next generation of AI and high-performance computing. Here are the key highlights:

Key Features of Blackwell:

1. Massive Scale: Blackwell chips are the largest chips ever made, with two of the largest dies connected together using a 10TB/s link. This allows for unprecedented computational power.

2. Reliability and Availability: Blackwell includes a Reliability and Availability (RAS) engine that can test every single transistor and memory element, improving uptime and stability for large-scale deployments.

3. Dynamic Precision Adaptation: Blackwell's second-generation Transformer Engine can dynamically adapt the precision of computations based on the required range and accuracy, improving efficiency.

4. Secure AI: Blackwell includes hardware-based security features to protect AI models from theft or tampering.

5. Compression Engine: Blackwell has a dedicated data compression engine that can pull data from storage 20x faster than before, improving data throughput.

Performance Improvements

• Blackwell offers a massive increase in AI performance, with up to 45x improvement over the previous generation.
• The energy required to train a 2 trillion parameter, 8 trillion token model has been reduced by 350x compared to the previous generation.
• Token generation performance has been improved by 45,000x, reducing the energy per token from 177,000 Joules to just 0.4 Joules.

Scalable Architectures

• Blackwell chips are combined into powerful DGX systems, with up to 72 GPUs connected using NVIDIA's advanced MV-Link interconnect.
• NVIDIA is also developing new high-speed Ethernet switches, called Spectrum, to enable seamless scaling to tens of thousands of GPUs and beyond.

Overall, Blackwell represents a major leap forward in GPU architecture, enabling unprecedented performance, efficiency, and scalability for the next generation of AI and high-performance computing applications.

The era of robotics has arrived. Researchers and companies around the world are developing robots powered by physical AI - models that can understand instructions and autonomously perform complex tasks in the real world.

Key advancements enabling this include:

1. Multimodal Large Language Models (LLMs): Breakthroughs in multimodal LLMs allow robots to learn, perceive, and understand the world around them, and plan how to act.

2. Reinforcement Learning from Demonstrations: Robots can now learn skills required to interact with the world by observing and learning from human demonstrations.

3. Reinforcement Learning in Simulation: Robots can learn skills through trial-and-error in simulated environments that obey the laws of physics, minimizing the "sim-to-real" gap.

NVIDIA has built Omniverse as the platform where these physical AI models can be created. In Omniverse, robots can learn how to autonomously manipulate objects, navigate environments, and more.

The key components are:

• NVIDIA AI Supercomputers to train the models
• NVIDIA Jetson Orin and next-gen Jetson Thor for running the models on robots
• NVIDIA Omniverse for simulating the robot learning and refinement

NVIDIA is partnering with leading companies like Siemens, Foxconn, and others to integrate this physical AI technology into real-world robotic factories and systems. The future of robotics, powered by physical AI, is here.

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Nvidia is at the forefront of the next wave of AI - physical AI that can understand and interact with the real world. Some key points:

• Nvidia has developed platforms like Omniverse to enable the creation of digital twins of factories, where robots powered by physical AI can learn and refine their skills through simulation.

• Nvidia's hardware and software stack, including chips like Blackwell and Jetson, provide the computational power and AI capabilities needed to enable these physical AI systems.

• Nvidia is partnering with leading industrial automation companies like Siemens to integrate its physical AI technologies into real-world robotic systems for factories and logistics.

• The future will see an explosion of robotic products, from self-driving cars to humanoid robots, all powered by Nvidia's physical AI technologies.

• Nvidia is providing the full stack - from hardware to software to pre-trained models - to enable companies to build these physical AI systems and integrate them into their operations.