NVIDIA GTC 华盛顿特区黄仁勋主题演讲
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会议摘要
Nvidia leads in AI, robotics, and computing advancements, collaborating with global partners to innovate 6G, quantum computing, and AI supercomputers. It emphasizes open-source AI, digital twin technology, and the integration of AI in robotics, manufacturing, and autonomous vehicles, aiming to revolutionize industries and contribute to the reindustrialization of America.
会议速览
The dialogue highlights groundbreaking advancements from semiconductor technology to supercomputers, emphasizing their pivotal roles in driving the digital revolution and setting the stage for future technological progress.
Highlights AI's pivotal role as transformative infrastructure akin to electricity and the internet, underscoring America's leadership in innovation and the launch of AI factories. Emphasizes collective effort towards clean energy, space exploration, and extending human capabilities, marking a new era akin to the Apollo mission.
At the GTC in Washington, DC, the company's CEO acknowledges the video presentation's impact, reflecting on personal pride in American technological advancements.
Nvidia highlights the shift from traditional computing to accelerated computing, emphasizing the role of GPUs and the necessity of redefining software for parallel processing. The company underscores its 30-year commitment to this innovative approach, now pivotal in overcoming the limitations of Moore's Law.
The dialogue emphasizes the critical role of CUDA and a suite of 350 libraries in transforming computing. By ensuring compatibility across generations and providing robust programming models, these libraries have enabled advancements in AI, computational lithography, quantum computing, and more, opening new markets and driving innovation in diverse fields.
A retrospective on NVIDIA's evolution from its inception in 1993, highlighting its contributions across various industries including AI, quantum models, and telecommunications, with gratitude expressed to employees and anticipation of future innovations and partnerships.
The dialogue emphasizes the need to reclaim American dominance in wireless technology, highlighting a partnership with Nokia to innovate and deploy American technology globally, aiming to leverage a platform shift in computing for national technological resurgence.
Nvidia introduces Arc, a new product line leveraging AI and accelerated computing for 6G, partnering with Nokia to integrate technology into base stations, enhancing spectral efficiency and enabling AI on wireless networks.
Quantum computing has achieved a significant milestone with the creation of a coherent, stable, and error-corrected logical qubit, a breakthrough 40 years in the making. This advancement necessitates the integration of quantum computers with GPU architectures to enhance error correction, AI calibration, and control. Various quantum computing methods, including superconducting, photonic, and trapped ion, are being explored, highlighting the importance of connecting quantum and classical computing resources for optimal performance in complex algorithmic operations.
Quantum computers face challenges with qubit fragility and error correction. NVQ Link introduces a groundbreaking interconnect architecture, directly linking quantum processors to Nvidia GPUs, enabling terabytes of data transfer for quantum error correction. This innovation not only enhances error correction but also facilitates orchestration between quantum devices and AI supercomputers, merging quantum and classical computing into a unified, accelerated supercomputing platform.
NVIDIA introduces NVQ Link for quantum computer control and scalability, backed by 17 industry partners and DOE collaboration. Announces 7 new AI supercomputers to lead U.S. science, integrating quantum, AI, and classical computing for enhanced research capabilities.
Discusses AI's evolution from hand-coded software to machine learning on GPUs, highlighting energy requirements, tokenization of various data types, diverse model architectures, and AI's transformative role from tools to active workers.
The dialogue highlights the transformative impact of AI across various sectors, from software development using Cursor to AI-assisted research and robo taxis. It emphasizes AI's role in augmenting human labor, driving economic growth, and addressing global labor shortages, marking a pivotal shift in how technology interacts with and enhances the global economy.
AI is emerging as a new industry, requiring specialized systems dubbed 'AI factories' to generate valuable tokens at high rates. Unlike traditional data centers, these factories focus solely on AI tasks, processing vast amounts of contextual information. This shift highlights the increasing demand for computational power to support AI applications, especially during peak usage, and underscores the need for efficient, high-performance systems.
Discusses AI's advancement from pre-training, akin to memorization and basic learning, to post-training, which focuses on problem-solving skills, reasoning, and computation, marking a significant shift in AI capabilities.
The dialogue explores the escalating computational demands driven by AI advancements and the virtuous cycle of smarter models leading to increased usage and higher compute requirements, highlighting the need for innovative solutions to sustain growth amidst the decline of Moore's Law.
NVIDIA has achieved a virtuous cycle with CUDA applications and AI, where increased usage and investment lead to smarter AI and more applications. To sustain this cycle, focusing on reducing costs is crucial to enhance user experience and maintain the momentum of AI development.
Extreme co-design is introduced as a method to overcome Moore's Law limits by integrating hardware, software, and system architecture innovations. Nvidia pioneers this approach, creating scalable AI supercomputers with custom-designed chips, systems, software, and networking technologies, achieving exponential performance gains.
A detailed exploration of advanced wafer-scale processing, showcasing the creation of a single, powerful GPU. The dialogue highlights the benefits of integrating numerous chips into one giant rack, achieving unprecedented processing capabilities. This innovation promises to revolutionize computing, demonstrating the incredible potential of unified processing power.
Expresses excitement about future advancements akin to Thor's abilities, emphasizing creation of Mv links nvlink 8, while noting current impracticality of dreams.
Grace Blackwell and NVLink 72 offer unparalleled efficiency in processing large AI models, achieving 10x performance improvement with half the transistor count, leading to lower cost token generation and optimized CapEx for cloud service providers.
NVIDIA's strategic shift from general to accelerated computing, underpinned by AI and non-AI applications, has triggered unprecedented growth. With a $500 billion revenue projection through 2026, driven by Blackwell and Rubin GPUs, the company celebrates its supply chain partners' hard work, showcasing a transformative impact on data processing and AI capabilities.
The dialogue highlights the creation of advanced AI supercomputers in America, detailing the manufacturing process from silicon wafers in Arizona to the assembly of components in Texas and California. It emphasizes the importance of extreme co-design, involving multiple chips, to exponentially increase performance and reduce costs. The narrative underscores America's re-industrialization through AI technology, showcasing the transition from a single chip to complex, multi-chip systems, and the commitment to continuous innovation in AI supercomputing.
The dialogue showcases Nvidia's advancements in AI infrastructure, introducing a new processor for enhanced memory management and context processing. It highlights the design of an AI factory, featuring liquid-cooled nodes, high-bandwidth switches, and the use of Nvidia Omniverse Dsx for digital twin simulation and optimization. Partnerships with companies like Siemens and Schneider Electric are emphasized for co-designing and operating these AI factories, aiming to deliver significant revenue through optimized power consumption and faster deployment.
Discusses NVIDIA's leadership in open source AI models, ecosystem growth, and collaborations with cloud providers and SaaS companies to enhance productivity and cybersecurity through AI.
A groundbreaking partnership between Palantir Ontology and Nvidia aims to transform data processing, enabling faster insights from structured and unstructured data for global enterprises and national security, achieving unparalleled scale and speed.
Nvidia's physical AI, leveraging three specialized computers and digital twin simulations, is transforming manufacturing through advanced robotics and AI integration, showcased in partnerships with Foxconn, Caterpillar, and Disney for reshoring and reindustrialization efforts.
The dialogue highlights Nvidia's advancements in digital twin technology and the Hyperion platform, designed to standardize autonomous vehicles, particularly robo taxis, aiming to transform global transportation with enhanced safety and efficiency.
Nvidia's strategic shift towards accelerated computing and AI marks a pivotal moment in technology. Key innovations include Arc for 6G, Hyperion for robotics, and Dsx and Mega for AI factories. Collaborations with enterprises like CrowdStrike and Palantir, alongside advancements in quantum computing and open models, underscore Nvidia's comprehensive approach to shaping the future of computing across diverse sectors.
A collective vision and shared determination fuel an unstoppable journey toward a brighter future, symbolized by dreams reaching the sky and a spark brighter than the sun.
要点回答
Q:What are the contributions of various technological innovations mentioned in the speech?
A:The innovations mentioned include Hetty Lamar reimagining communication for wireless connectivity, IBM's System 360 introducing a universal computer to industry, Intel's microprocessor driving the digital age, and Cray's supercomputers expanding the frontiers of science.
Q:What role did Apple play in the development of computing and the internet?
A:Apple revolutionized computing with the creation of the Macintosh and made the internet accessible through software before the web's widespread adoption, and later put 1,000 songs in a pocket with the iPod and the internet in a mobile device with the iPhone.
Q:What is considered the most important contribution to the computer industry and why?
A:The most important contribution to the computer industry is believed to be machine learning, which is a form of artificial intelligence that allows computers to almost appear to think. It is expected to be a revolution, driven by computational resources.
Q:What is the significance of the new computing model Nvidia has invented?
A:Nvidia has invented a new computing model that is significant because it aims to solve problems that general-purpose computers cannot, as transistors reach the limits of Moore's Law. This new model is based on accelerated computing and requires a different programming approach, such as developing new algorithms and libraries, which has taken nearly 30 years to achieve.
Q:What has Nvidia achieved with the CUDA programming model?
A:Nvidia has achieved the development of the CUDA programming model, which is compatible across generations, and has enabled hundreds of millions of GPUs to run perfectly in every computer. This compatibility and dedication to the programming model have been key to Nvidia's success.
Q:What new partnership does Nvidia have, and what is its goal?
A:Nvidia has partnered with Nokia, the second largest telecommunications maker in the world, to integrate Nvidia's technology into Nokia's base stations. The goal is to use this partnership to build a new 6G wireless communication system based on accelerated computing and AI, thereby upgrading millions of base stations globally and enhancing spectral efficiency and AI for Radio Access Network (RAN) functionalities.
Q:What are the different types of quantum computers mentioned?
A:The different types of quantum computers mentioned are superconducting, photonic, trapped ion, stable atom, and they all use qubits as their core building blocks.
Q:What challenge do all qubits share, and what is the solution?
A:All qubits share the challenge of fragility and extreme sensitivity to noise, where they remain stable for only a few hundred operations. The solution is quantum error correction, which uses entangled extra qubits to determine where errors occurred without damaging the qubits we care about.
Q:What does the new interconnect architecture, NVQ Link, facilitate and what is its scalability potential?
A:The new interconnect architecture, NVQ Link, facilitates direct connections between quantum processors and Nvidia GPUs for error correction, and it also enables the control, calibration, and simulation of quantum computers. It is scalable, designed to handle error correction for the current few qubits as well as the future scaling up to tens of thousands and even hundreds of thousands of qubits.
Q:How is AI different from a chatbot, and how is it reinventing the computing stack?
A:AI is much more than just a chatbot. While chatbots are at the forefront of public perception of AI, AI's scope extends to completely reinventing the computing stack, moving away from hand-coded software to machine learning, data-intensive programming that runs on GPUs. This requires a fundamental change in the computing stack, emphasizing the importance of energy, GPUs, and the infrastructure supporting AI.
Q:What are the components of the AI stack, and how do they enable AI to work with different types of data?
A:The AI stack comprises GPUs, infrastructure like data centers, energy, and AI models. These components enable AI to work with various types of data by transforming data into tokens, which are like computational units or vocabulary of AI. This tokenization process allows AI to learn, translate, and respond to the meaning of this 'language', enabling it to generate outputs similar to ChatGPT, but for different types of data like proteins, chemicals, and 3D structures.
Q:How is AI being applied in different industries, and what is the role of AGI in this context?
A:AI is being applied in various industries such as chatbots, digital biology, and self-driving cars, among others. AGI (Artificial General Intelligence) is considered fundamentally critical, along with deep computer science and computing breakthroughs, for developing AGI. AI is also envisioned as workers that can use tools, as opposed to being a tool itself, indicating its role as a partner in the workforce that can perform tasks and use tools to enhance productivity.
Q:What is the concept of an AI factory, and how does it differ from traditional data centers?
A:The concept of an AI factory involves a new type of system that is specialized for generating tokens based on the context of AI usage. Unlike traditional data centers that are general-purpose and run various applications, an AI factory is dedicated to the context processing and token generation needed for AI, making it different from the universal general-purpose computers of the past.
Q:What is the purpose of an AI factory and what is it designed to produce?
A:The purpose of an AI factory is to produce tokens that are as valuable as possible, meaning the AI's output needs to be smart and produced at incredible rates.
Q:What breakthroughs have been made in AI technology in recent years?
A:Recent breakthroughs include figuring out how to make AI much smarter than just through pre-training, and the introduction of post-training, which focuses on teaching skills to solve problems, such as reasoning and computation.
Q:Why is post-training crucial for AI development?
A:Post-training is crucial because it teaches AI skills to solve problems, break down problems, reason about them, and use first-principle reasoning, which enables more complex and intelligent problem-solving.
Q:How much computation is necessary for AI and what has changed in this regard?
A:AI requires an enormous amount of computation, with post-training using even more computation than pre-training. The computational demands have increased due to the scaling laws that result in smarter models, which need more compute but also provide more intelligence.
Q:What is the relationship between smarter AI models, usage, and the required computation?
A:The smarter the AI models become, the more people tend to use them. This leads to a positive feedback system where more usage results in more computing needs, creating a virtuous cycle.
Q:How has the AI industry's demand for compute resources changed?
A:The AI industry's demand for compute resources has increased significantly as AI models have improved and become worthy of paying for, which has created two exponential growth curves: one from the scaling laws and the other from increased usage.
Q:What is the 'virtuous cycle' that has been achieved by the AI industry?
A:The 'virtuous cycle' refers to the feedback system where the more an AI is used and paid for, the more profit is generated, leading to more computational resources being invested in AI factories, which makes the AI smarter, and in turn, encourages more usage.
Q:Why is it necessary to drive down the costs of AI?
A:It is necessary to drive down costs to improve user experience, such as by allowing faster responses from AI, and to maintain the virtuous cycle that is essential for the growth and productivity of the AI industry.
Q:What is co-design and why is it critical for advancing AI?
A:Co-design is a process that involves designing chips and considering all aspects of the stack, from computer architecture to software and applications, to meet the exponential growth in AI requirements. It's critical because it allows for the compound exponential growth needed to sustain the AI industry's advancements.
Q:How does Nvidia innovate in the field of AI technology?
A:Nvidia innovates by starting from a blank sheet of paper to rethink every aspect of AI technology, including new computer architecture, chips, systems, software, model architecture, and applications. They also scale their solutions up and out to create larger data centers and interconnectivity, applying extreme co-design principles.
Q:What performance benefits does the new AI system architecture offer?
A:The new AI system architecture offers performance benefits that are much greater than linear improvements, similar to the dramatic advancements seen in IBM's System/360 models. The architecture allows for faster response times and more efficient use of computational resources.
Q:What are the top six CSPs planning to invest in CapEx?
A:The top six CSPs that are planning to invest in CapEx are Amazon, Core Weave, Google, Meta, Microsoft, and Google.
Q:Why is Nvidia's GPU preferred over ASIC for certain tasks?
A:Nvidia's GPU is preferred over ASIC for certain tasks because it can do all kinds of data processing, image processing, computer graphics, computation, and, notably, AI. ASICs may be able to do AI but not all the aforementioned tasks, which is why Nvidia's architecture is preferred.
Q:What are the reasons for the remarkable growth in Grace Blackwell's sales?
A:The remarkable growth in Grace Blackwell's sales is attributed to the transition from general purpose computing to accelerated computing and the anticipation of demand from AI. The transition is supported by a wide range of applications, and AI is a significant factor driving the demand for accelerated computing.
Q:What is the expected growth and revenue for Nvidia's Grace Blackwell?
A:Nvidia expects extraordinary growth for Grace Blackwell, driven by two exponential factors. They have visibility into half a trillion dollars of cumulative revenue for Grace Blackwell and early ramps of rein through 2026. As of the first few quarters of production, they have shipped 6 million units, with another quarter's worth of inventory left for 2025. They anticipate an additional half trillion dollars in revenue from 2025 to 2030, which is five times the growth rate of Hopper.
Q:How is Grace Blackwell produced and what technology does it use?
A:Grace Blackwell is produced through a complex process involving hundreds of chip processing steps, ultraviolet lithography, assembly of HBM stacks, and sophisticated packaging techniques including the chip on wafer on substrate process. It uses advanced EUV technology and is built with a combination of Blackwell dies, HBM stacks, and various interconnects and processors. The assembly involves careful integration of multiple chips and components to create a highly integrated and functional AI supercomputer.
Q:What advancements have been made in AI computing since the inception of Nvidia?
A:Since the inception of Nvidia, there have been incredible advancements in AI computing. The progression from a single chip that could perform 9 Petaflops to the Vera Rubin, which offers 100 Petaflops, showcases a 100 times increase in performance over a short period. These advancements include not just increases in computational power but also in system architecture and integration, as evidenced by the transition from chip design to system design and the development of entire AI factories.
Q:What is the significance of the Vera Rubin computer and its performance?
A:The Vera Rubin computer is significant as it represents the third generation of the GB 200 system and represents an enormous leap in performance, being 10 times faster than the previous model. It features revolutionary design elements like being completely cableless and liquid-cooled, and it supports more advanced context processing for AI, enhancing the speed and efficiency of data retrieval and response times.
Q:How is the AI factory of the future envisioned and what technologies are involved?
A:The AI factory of the future is envisioned as an ecosystem-scale challenge requiring collaboration among hundreds of companies. It involves the use of Nvidia's AI infrastructure stack, which includes the Omniverse digital twin for optimization and simulation, and prefabricated modules for rapid deployment. This approach allows for faster time to revenue by integrating various components and services into a cohesive, scalable solution.
Q:How can AI factory optimizations lead to additional revenue?
A:Optimizations provided by AI factories, such as those enabled by Dsx, can deliver billions of dollars in additional revenue per year across states like Texas, Georgia, and Nevada.
Q:What is the significance of using a digital twin in the early stages of AI factory development?
A:The digital twin is significant in allowing the planning, design, optimization, and operation of AI factories before they are even built, using its capabilities as a virtual representation.
Q:Where is Nvidia building an AI factory research center?
A:Nvidia is building an AI factory research center in Virginia using Dsx to test and productize Vera Rubin from infrastructure to software.
Q:How are open source models impacting AI development and startups?
A:Open source models have become quite capable due to advances in reasoning capabilities, multimodality, and efficiency through distillation. They are now a vital resource for developers, startups, and various industries, making it possible to embed domain expertise into models.
Q:Why is it important for the United States to lead in open source?
A:It is important for the United States to lead in open source because open source models are critical for researchers, developers, startups, and companies across various industries, and the country's and its startups' reliance on these models necessitates U.S. leadership in open source development.
Q:What are the capabilities of Nvidia's AI models?
A:Nvidia's AI models are number one in speech, reasoning, and physical AI, with a large number of downloads. They are backed by Nvidia's commitment to contribute to open source and its extensive library of models.
Q:Why do AI startups choose to build on Nvidia?
A:AI startups choose to build on Nvidia because of its rich ecosystem, easy-to-use tools, availability of GPUs in various environments, and the continuous improvement of the developer community.
Q:How does Nvidia integrate its libraries with major cloud providers?
A:Nvidia integrates its Cudamani libraries and open source AI models into major cloud providers such as AWS, Google Cloud, and Microsoft Azure, ensuring that the Nvidia stack works seamlessly wherever the computing resources are used.
Q:What is the goal of the partnership between Nvidia and CrowdStrike?
A:The goal of the partnership between Nvidia and CrowdStrike is to create a system that supercharges cybersecurity with AI, providing speedy detection and response capabilities to protect against threats.
Q:What is the significance of Palantir Ontology and Nvidia's integration with it?
A:Palantir Ontology is a business insight platform that turns information, data, and human judgment into actionable insights. Nvidia's integration with Palantir Ontology aims to accelerate data processing and find insights from diverse datasets, including structured, human-recorded, and unstructured data.
Q:What are the three computers required for Physical AI?
A:For Physical AI, three computers are required: one for training the model, another for simulations using Omniverse Dsx, and a robotic computer that operates the robots within the digital twin.
Q:How are digital twins being utilized in the manufacturing sector?
A:Digital twins are utilized in the manufacturing sector to plan, design, and operate AI factories and robots. They help in creating a state of the art robotic facility, validating systems before construction, optimizing layouts, training robots, simulating operations, and monitoring and alerting of anomalies or quality issues.
Q:What are the future applications of human-robot interaction?
A:Future applications of human-robot interaction include advanced manufacturing, where robots work alongside humans, warehouse automation, performing noninvasive surgery with precision, and consumer electronics markets with humanoid robots.
Q:What is the importance of the Nvidia Drive Hyperion system?
A:The Nvidia Drive Hyperion system is important because it enables the creation of vehicles that are robo taxi-ready, providing a standard platform for developing AI systems that can be deployed across various vehicle types, leading to a computing platform on wheels.
Q:What is the relationship between the second platform transition and the growth of Nvidia?
A:The second platform transition, which is the shift from classical software to AI-driven platforms, coincides with the growth of Nvidia as both transitions are happening simultaneously.
Q:What new platforms are introduced by Nvidia, and what are their purposes?
A:Nvidia has introduced new platforms such as Hyperion for cars, Dsx and Mega for AI factories, and Arc for robotics. These platforms are designed to address various industry needs, including advanced computing, AI integration, and robotics.

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