Jensen Huang: "This New Invention Will ABSOLUTELY Destroys The Whole Competition!"

Jensen Huang:

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Supercomputers have made a significant leap  forward with powerful artificial intelligence,   revolutionizing software development. Engineers   and supercomputers are now collaborating  to achieve feats once thought impossible,   an exciting yet unsettling prospect. At  the heart of this transformation is the   immensely complex H100 computer. With these  rapid advancements, what happens if these  

machines start acting autonomously? Let us  explore how small humanity appears when faced   with the record-breaking 5000% expansion  in the power of the most advanced AI chip. A significant shift has occurred in the  ever-evolving landscape of technology,   marked by the rise of accelerated computing and  the integration of artificial intelligence into   the very fabric of software development. This  shift has transformed the role of computer   engineers, who now collaborate with AI  supercomputers to push the boundaries   of what machines can do. The atmosphere in  the tech industry is charged with a mix of   anticipation and skepticism, as these advances  promise to reshape every sector imaginable. At the heart of this technological revolution is  the H100, a computer that has been heralded as a   game-changer. Its production process  is a marvel of modern engineering,  

involving a system board that houses 35,000  components and is powered by eight advanced   Hopper GPUs. This complexity is not just  a showcase of human cleverness but also   a humbling reminder of our growing  dependence on high-tech machines. The H100 itself is a behemoth, weighing between  60 to 65 pounds. Its assembly requires robotic   precision due to the high-pressure demands of  fitting its numerous parts perfectly together.   Ironically, despite its capabilities, the computer  cannot be assembled without the aid of other   machines. This dependency underscores  a broader theme in modern technology:   the creation of devices that are  as needy as they are powerful.

With a staggering price tag of $200,000, the  H100 replaces entire rooms of older computing   equipment, earning it the title of the world’s  most expensive computer. The manufacturers tout   economic efficiency with a twist—buy  more to save more—a pitch that might   raise eyebrows considering the  upfront investment involved. Beyond its commercial appeal, the H100 is  pioneering in its inclusion of a Transformer   engine, setting new standards for AI-driven  computing performance. This leap forward comes   at a time when the computer industry is facing  a critical junction. The traditional reliance   on CPU scaling to improve performance has  reached its limits. The familiar pattern  

of gaining significantly more power at the same  cost every five years has come to an abrupt halt. This stagnation has resulted in two major  trends within the industry. The first is a   pivot towards new technologies like the H100  that promise to continue the trajectory of   rapid advancement. The second is the increasing  embrace of AI as not just a tool but a partner in   creation, reflecting a deeper integration of  technology into the creative process itself. The promise of these technological advances is  immense, suggesting a future where computers   are not only tools but collaborators capable  of driving innovation to new heights. Yet,   this future also brings challenges,  as the increasing complexity of   technology could potentially lead to greater  complications. As this narrative unfolds,  

the tech industry must navigate these  waters carefully, balancing innovation   with a critical awareness of the broader  impacts of these powerful new tools. A world rapidly transforming through the  innovations of technology, the introduction   of deep learning marked a significant  turning point. This method revolutionized   the way software was developed, coinciding  with the rise of accelerated computing and   generative AI. Together, these advancements  reshaped the entire computing landscape,   a process that had taken nearly thirty years  of meticulous development and refinement. This shift was akin to replacing old,  bulky television sets with sleek,   modern LEDs—a stark improvement in efficiency  and output for the same price. It underscored   the importance of GPU servers, known for  their high cost but essential for modern   computing. In this new era, the focus shifted  from individual servers to the optimization of  

entire data centers. Building cost-effective  data centers became the new challenge,   overshadowing the previous goal  of optimizing individual servers. Attention to individual server performance  sufficed. However, the evolution of technology  

and increasing demands meant that the broader  infrastructure, the data center itself,   became the critical focus. Compact, high-speed  data centers were now the goal, rather than merely   large ones. An illustrative example of this was in  achieving specific workloads, dubbed ISO work. The   improvements brought by accelerated computing in  this area were nothing short of transformative,   offering a clear before-and-after  picture of technological evolution. Nvidia's GPUs, originally perceived as  limited to specific computational tasks,   had proven themselves to be versatile  and indispensable. They now played a   pivotal role in modern computing, pushing  forward the capabilities in generative AI   and beyond. The widespread adoption and  high utilization rates of these GPUs in   every cloud and data center pushed  these facilities to their limits,   highlighting the immense impact and necessity  of GPUs in contemporary computing setups.

This new age was defined not merely by  the hardware used, but by the limitless   potential for innovation and improvement.  As GPUs continued to evolve and adapt,   they propelled the computing industry  forward into an era where technological   boundaries seemed ever-expandable,  promising a future of endless   possibilities and advancements. Now, let's  see how 'Nvidia AI' is changing technology. The Rise of AI Supercomputing A revolution was underway with  the transformation of the GPU,   now designed to excel in tensor processing, a core  element of AI operations. This revamped system,   known as 'Nvidia AI,' was engineered to  manage an entire spectrum of tasks—from   initial data handling through to  training, optimizing, deployment,   and inference—integrating all phases of  deep learning that underpin modern AI. These GPUs were interconnected using a technology  called NVLink to enhance their capabilities,   amalgamating them into a colossal  single GPU. This setup was further   expanded by linking multiple such units  with InfiniBand, creating vast, powerful   computing networks. This strategic expansion  was not just for show; it played a pivotal role  

in enabling AI researchers to push forward with  developments in AI technology at an astonishing   pace. With each two-year cycle, the field  witnessed significant leaps, propelling the   technology forward, with the promise of more  groundbreaking advancements on the horizon. Looking ahead, it was envisioned that every major  company would soon operate its own AI factory,   dedicated to creating its distinct type of  intelligence. Previously, intelligence creation  

was an inherently human endeavor. However, the  future landscape was shaping up differently, with   the mass production of artificial intelligence  becoming a norm. Each company was expected to   establish its AI production line, significantly  altering the traditional business operations. The rise of AI was seen as the next monumental  phase in computing for several reasons. Using   AI and accelerated computing, technological  advancements had enabled the speeding up of   computer graphics processing by an unprecedented  thousand times in merely five years. Moore's Law,   which once posited that computing power  would double approximately every two years,   now seemed almost obsolete in comparison.  The implications of increasing computing  

power by a million times in ten years were  profound. Such immense power opened up   possibilities for applying computing  technology to previously inaccessible fields. The widespread enthusiasm for these  advancements was justified. Each new  

computing era had historically enabled  previously impossible capabilities,   and this era was proving to be no exception.  What set this era apart was its ability to   process and understand diverse types of  information, not just text and numbers.   This capability made modern AI invaluable  across various industries. Moreover,   the new generation of computers was designed to  adapt to the programming language or style used,   striving to decipher the user's intent through  extensive, sophisticated neural networks.

The narrative unfolding was one of  vast technological strides alongside   an evolution in industry, painting a future  where companies not only adapted to but   fully embraced the surge in computational  power to redefine possibilities. This era   promised not just technological growth but a  redefinition of global industrial landscapes. Each new advancement seems to arrive with  a wave of grand claims about its potential.   One of the latest developments is a language  model that boasts of being so intuitive that   practically anyone could master programming  simply by giving voice commands. The claim   was that the divide between those who  were tech-savvy and those who weren't   had finally been bridged. However, beneath  the surface of this seeming simplicity,   the complexities of power dynamics in  technology persisted, largely unchanged.

Old software applications, ranging from basic  office tools to more complex web environments,   were now being marketed as rejuvenated by  the integration of artificial intelligence.   The prevailing message was clear: there was no  need for new tools when the old could simply be   updated with AI capabilities. Yet, the reality  of these enhancements was not as simple as it   was portrayed. Adapting traditional software  to function with AI often required significant   modifications to their foundational architecture,  sometimes necessitating complete overhauls. The Nvidia Grace Hopper AI Superchip served  as a prime example of this new technological   era. This processor, packed with  nearly 200 billion transistors,  

was not just a component but was heralded  as the cornerstone of future computing.   With 600 gigabytes of memory designed  to seamlessly connect the CPU and GPU,   it promised a new level of efficiency by reducing  the need to transfer data back and forth. This superchip was touted as a revolutionary  solution for handling extensive data sets with   unprecedented speed and efficiency. However, it  also posed questions about the monopolization of  

power within the tech industry. What would  the concentration of so much computational   capability in a single chip mean for market  competition and innovation? While the discussions   frequently highlighted the capabilities of  such technologies, they seldom addressed the   environmental impacts and ethical considerations  of producing and operating these advanced systems. The vision for the future did not stop at a  singular chip. Plans were laid out to link  

multiple Grace Hopper chips together to  form a formidable network. Envisioning a   setup where eight of these chips were connected at  extraordinary speeds and organized into clusters,   the structure was designed to scale up by  interlinking these clusters into a massive   computational network. This ambitious design  showcased an impressive level of connectivity   and operational speed but also underscored an  insatiable drive for more power and efficiency,   which often overshadowed the underlying  issues of complexity and opacity   in such expansive systems. Next, we look at  the impact of these technologies on people. The Hidden Costs of Supercomputing As these supercomputers expanded and  interconnected, the individual's role   seemed to diminish. The notion of being a  programmer was shifting away from innovative   coding towards maintaining and feeding  data into these vast, powerful systems.  

The narrative that AI could enhance every  existing software application was appealing,   yet it seemed more likely to reinforce existing  technological hierarchies than to disrupt them. While a marvel of engineering, the Grace  Hopper required a discerning evaluation. Its   capabilities were undoubtedly impressive, yet it  was crucial to consider who benefited from these   technological advances and at what societal cost.  The transformation of the technological landscape   was not merely a matter of hardware and software  advancements but involved significant social and   economic shifts. As these changes unfolded, there  was a risk that individuals could become mere   bystanders or even subordinates to the formidable  machines that their own ingenuity had created. Let's visualize what this looks like. Imagine  a giant setup stretching over your entire  

infrastructure, made up of 150 miles of fiber  optic cables—enough to span several cities.   Inside, 2,000 fans blast air powerfully,  circulating 70,000 cubic feet per minute,   which could refresh the air of a large  room in just moments. The total weight?   Forty thousand pounds, comparable to four adult  elephants, all packed into the space of one GPU.

This enormous machine is the Grace Hopper AI  supercomputer, named to inspire thoughts of   innovation and pioneering work. It's a single,  giant GPU, and building it has been a massive   undertaking. Nvidia is currently assembling this  giant, and soon, tech giants like Google Cloud,   Meta, and Microsoft will use it to explore  new areas of artificial intelligence. The DGX GH200, its official name, represents  Nvidia's ambition to push beyond the current   limits of AI. Nvidia aims to transform  data centers worldwide into 'accelerated'  

centers equipped for the new era of  generative AI over the next decade. To address these varied needs, Nvidia has  introduced the Nvidia MGX, an open modular   server design created in partnership with several  companies in Taiwan. This new server design is a   blueprint for the future of accelerated  computing. Traditional server designs,   made for general computing, don't meet the needs  of dense computing environments. The MGX design is   a model of efficiency, combining many servers  into one powerful unit. This not only saves  

money and space but also provides a standardized  platform that will support future technology like   next-generation GPUs, CPUs, and DPUs. This means  that investments made today will continue to be   useful in the future, fitting new technologies  with ease and keeping up with market demands. The chassis of the MGX serves as a base on  which different configurations can be built,   tailored to meet the diverse needs of various  data centers across many industries. This design   flexibility is crucial in a field that changes as  quickly as AI does. Nvidia's strategy is clear:   to lead the way in computing towards a more  unified, efficient, and powerful future,   ensuring that investments grow and adapt,  potentially transforming the way the world works.

The Grace Superchip server,  focused solely on CPU power,   can host up to four CPUs or two of its  Superchips, boasting that it delivers   great performance while using much less  energy. It claims to use only 580 watts,   half of what similar x86 servers use, which  is 1090 watts. This makes it an attractive   option for data centers that need to save  on power but still want strong performance.

The discussion shifts to how networks within these  centers are becoming the backbone of operations.   There are two main types of data centers.  The first is the hyperscale data center,   which handles a variety of applications, doesn’t  use many CPUs or GPUs but has many users and   loosely connected tasks. The second type is the  supercomputing centers, which are more exclusive,   focusing on intensive computing tasks with  tightly linked processes and fewer users.

Ethernet connectivity is highlighted as a key  component that has shaped the development of   the internet thanks to its ability to link  almost anything effortlessly. This attribute   is essential for the widespread and diverse  connections across the internet. However,   in supercomputing data centers, where  high-value computations are the norm,   this kind of random connectivity is not feasible.  In these settings, where billions of dollars’   worth of technology is at stake, connections  need to be carefully planned and managed to   ensure optimal performance. The flexibility of  Ethernet, while revolutionary for the internet,  

must be more controlled and precise  in these high-stakes environments. Networking throughput is vital  in high-performance computing,   essentially worth $500 million. It's crucial  to note that every GPU in the system needs to   complete its tasks for the application  to progress. Normally, all nodes must  

wait until each one has processed its data. If  one node is slow, it holds up everyone else. The challenge is how to develop a new type of  Ethernet that integrates with existing systems   while enhancing performance to meet high  demands. This new Ethernet would need to   be designed to handle large amounts of  data and synchronize activities across   all nodes effectively, preventing  delays caused by any single node.   Let's dive into the networks that  support these powerful computers. The Practical Limits of Self-Managing Networks Such innovation isn't just about meeting  current needs but about advancing network   capabilities. The difficulty lies  in the technical development and  

in ensuring compatibility with older  systems, which millions depend on. Introducing this advanced Ethernet would  transform high-performance computing,   making it more efficient. However, this  improvement depends on both technological   advancements and a shift in how we view and  implement network systems. Embracing this   change requires investing in infrastructure  that supports long-term technological growth. Adaptive routing is promoted as a smart  solution for modern data centers. It aims   to manage data traffic by detecting which parts  of the network are overloaded and automatically   sending data through less busy routes. The idea  is that this process happens without the central  

processing unit intervening. On the receiving end,  another piece of hardware, known as Bluefield 3,   puts the data back together and sends it off  to the GPU as though everything is normal. Then there’s congestion control, which is  somewhat similar to adaptive routing but   focuses more on monitoring the network to prevent  any part of it from getting too congested with   data traffic. If a particular route is too busy,  the system directs the data to a clearer route.

However, the effectiveness of these technologies  in real-life situations is often not discussed   in detail. How well can these systems truly  operate without human help? The promise of "no   CPU intervention" sounds great, but is it really  practical, or just a way to sell more advanced,   and perhaps not always necessary, technology  to data centers? While the concept suggests   a network that manages itself, avoiding traffic  jams on its own, there are likely many practical   challenges that aren’t addressed. Real-time  data traffic can be complex and might still   need some traditional handling to  ensure everything runs smoothly. New solutions are constantly presented as the  ultimate fix to longstanding issues. One of the  

latest innovations is a sophisticated system  designed to manage network traffic through a   combination of advanced software and switches.  This system promises to significantly enhance   Ethernet performance by directing the flow  of data more efficiently across data centers.   The idea is to prevent network congestion  by having the system act as a controller,   instructing when to halt data flow  to avoid overloading the network.   This sounds promising on paper, yet  those familiar with network management   understand that the real-world application  often falls short of theoretical models. Meanwhile, a separate discussion has been gaining  momentum about the potential of GPUs overtaking   CPUs in handling artificial intelligence tasks.  Proponents of this shift highlight the superior  

data processing capabilities of GPUs, which  can handle multiple operations simultaneously,   making them ideal for certain types of  computations required in AI. However,   this enthusiasm often overlooks the nuances of  computational needs where CPUs might still hold   an advantage due to their general versatility  and efficiency in sequential task processing. Amidst these technological debates, there's an  unusual confidence placed in a single software   stack, touted as the only option secure and  robust enough for enterprise use. This claim   suggests a one-size-fits-all solution to diverse  corporate security and operational requirements,   a concept that oversimplifies the complex  landscape of business technology requires.   Such a sweeping endorsement can lead to  an overreliance on a single technology,   potentially sidelining other viable options that  might better meet specific organizational needs.

As these discussions unfold, it becomes clear  that while advancements in technology offer   potential benefits, there's a gap between the  ideal outcomes presented and the practical   challenges of implementation. This disparity is  often glossed over by those eager to promote the   next big solution. In reality, integrating  new technologies into existing frameworks   presents a multitude of challenges, from  compatibility issues to scalability concerns,   which can dilute the effectiveness  of even the most promising solutions. Thus, while the allure of new technology  is undeniable, a realistic perspective is   crucial. It's important to temper excitement with  a critical evaluation of how these technologies   perform outside controlled tests and in the  diverse environments they're intended for. This  

approach ensures a more grounded expectation  and a better understanding of how technology   can genuinely contribute to advancements  in data management and AI processing. Where security, management, and support  are key, Nvidia's latest move is quite   bold. The company has announced it will  take care of over 4,000 software packages,   covering everything from data processing  and training to optimization and making   decisions based on data. This mirrors what Red  Hat has done for Linux, but Nvidia is applying   it to all its own software, aiming to provide a  comprehensive, top-tier service for businesses. Let's consider what this really entails in  the realm of accelerated computing. Nvidia  

AI Enterprise is not just offering a broad range  of tools; it promises to manage and maintain this   extensive suite with a high level of care,  akin to well-known enterprise services. The   challenge here is immense. How will Nvidia ensure  smooth integration across such a wide variety of   software? Can they truly deliver the personalized  support that big businesses need, without becoming   cumbersome and complex? We continue exploring  how these changes affect who controls technology. AI, Nvidia, and the New  Power Dynamics of Technology  While potentially making things easier by reducing  the hassle of handling many software tools, this   strategy also brings up concerns about flexibility  and being tied to one vendor. Companies are pushed   toward a more uniform computing environment,  which might limit innovation from smaller software   creators and reduce the variety of technologies  that could lead to major new discoveries. Nvidia's claim of offering a business-grade  solution is both a sign of their dominance in   the market and a strategic move to embed  themselves deeply into the operations of   their customers. As companies increasingly rely  on fewer providers for their computing needs,  

one must wonder if the future of business  computing will be shaped more by technological   innovation or by the controlling  influence of a few powerful companies. The promise of faster computing through  GPUs is often highlighted as a major   benefit for enterprises. We're told that  using Nvidia's AI Enterprise on GPUs can   process 31.8 images per minute, which is  24 times faster than traditional CPUs,  

and it costs only five percent of the  usual amount. But there's a catch. This impressive boost in speed and cost-saving  isn't available to all businesses. The key   to unlocking these benefits is a secure  software stack from Nvidia AI Enterprise,   now fully integrated with big cloud  providers like AWS, Google Cloud,   and Microsoft Azure. This might seem helpful, but  it also raises a question: Is this integration   meant to spread advanced computing, or is it  a strategic move to make Nvidia’s technology   a core part of business infrastructure,  making it hard to choose anything else? Let's look closer. The enterprise world  is often tempted with stories of how new   technologies can change everything. But  what's really happening is not just about   improving technology—it's about changing  who has control in the tech industry.

Nvidia isn’t just selling faster processing.  They're creating a new dependence.   AI needs a different way to handle tasks,   and this isn’t just about better technology  but about shifting power within the industry. This transformation is driven by two main things:  faster computing and AI that can create content.   These are supposed to redefine what machines can  do. However, with all this talk of innovation,   one has to think about what it means when these  technologies are mostly controlled by a few big   companies and Nvidia. Their integration into major  cloud platforms doesn't just make things easier;   it might also lock advanced capabilities  behind the doors of these big players.

As these systems become more interconnected,  businesses might enjoy the immediate benefits   of speed and efficiency but could end up losing  much of their control over their operations.   This trade-off is often overlooked amidst the  exciting stats and promises of change. Nvidia's   collaboration with a hundred other software  companies under Nvidia Enterprise isn’t just   about technical partnerships; it's about weaving  a network that might be difficult to escape from. While everyone is focused on the advancements in  speed and AI, businesses need to understand the   full picture. In a world where fast computing  and AI skills are seen as key to success,   the real challenge might be about who controls  the technology that drives these advancements.

A notable shift was taking place in  the evolving landscape of technology.   Traditional computing, once dominated by  general-purpose machines, was making way   for a new paradigm that integrated the entire  system right up to the scale of data centers.   These were not just clusters of machines; they  were comprehensive systems crafted specifically   for various industrial sectors. Each sector  now boasted its own customized software stack,   tailored to maximize the efficiency  of these colossal computing entities. One of the central pieces of this  transformation was the HGX H100,   heralded as the core of generative AI  technology. Its purpose was straightforward:  

to enhance efficiency and drive down costs,  purportedly delivering unmatched value. This   technology was destined to serve AI factories  and was augmented through the Grace Hopper   system. This system was connected to a  vast network, an assembly of 256 nodes,   culminating in the creation of the DGX GH200,  the largest GPU ever constructed. The ambitious  

objective was to usher in a new era of accelerated  computing that would permeate various industries. The vision didn’t stop at merely enhancing  AI factories or large-scale operations. It   extended its reach into the cloud, to transform  every cloud data center into a generative AI   powerhouse. This endeavor was propelled by  Spectrum X, a system that was foundational  

to this transformation and depended on  four essential components: the switch,   the Bluefield 3 NIC, the vital high-speed  interconnects—including the indispensable   cables—and a comprehensive software stack  that orchestrated the entire operation. The strategic ambitions stretched even further,   aiming to penetrate global enterprises  with a variety of server configurations.   This expansion was facilitated through strategic  partnerships with Taiwanese technology leaders,   leading to the development of MGX modular  accelerated computing systems. These systems   were designed to integrate Nvidia technology  seamlessly into the cloud, enabling enterprises   around the globe to implement generative  AI models in a secure and efficient manner. As this technological revolution unfolded, it was  becoming clear that the shift toward tailored,   industry-specific computing frameworks had  significant implications. While the promise  

of enhanced performance and cost savings was  enticing, it also increased the dependency of   companies on specific technologies and vendors.  This burgeoning reliance could potentially   restrict flexibility and stifle innovation  in a market known for its rapid evolution. Despite these concerns, the drive toward  specialized, comprehensive computing   environments continued to redefine the approach  of businesses toward technology utilization.  

The integration of advanced technologies  with commercial strategies was shaping a   new era in business computing, influencing how  enterprises leveraged technology to achieve their   goals. The journey was complex and filled  with challenges, but the potential rewards   promised a new frontier in efficiency  and capability in the business world. What are your thoughts on the risk of  stifling innovation due to increased   dependency on specialized technologies? Like,  comment, and subscribe to join the discussion!

2024-06-04 14:47

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