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A PATTERN OF POSITIONING -- Nvidia CEO, Jensen Huang has made a pattern of positioning Nvidia in front of every big tech trend. • In 2012 a small group of researchers released a groundbreaking image recognition system, called AlexNet, that used GPUs, instead of CPUs, to crunch its code and launched a new era of deep learning. Huang promptly directed the company to chase AI full-steam. • When, in 2017, Google released the novel neural network architecture known as a transformer—the T in ChatGPT—and ignited the current AI gold rush, Nvidia was in a perfect position to start selling its AI-focused GPUs to hungry tech companies. • Nvidia now accounts for more than 70 percent of sales in the AI chip market. Its revenue for the last quarter of 2023 was $22 billion—up 265% from the year prior. And its stock price has risen 231% in the last year. Huang is either uncannily good at what he does or ridiculously lucky—or both!—and everyone wants to know how he does it. Tech companies can’t get enough of this tech company. Earnings are off the charts. WIRED probes the mind of its CEO, Jensen Huang. 👁️ Nvidia is building a new type of data centre called AI factory. Every company—biotech, self-driving, manufacturing, etc will need an AI factory. 👁️ Jensen is looking forward to foundational robotics and state space models. According to him, foundational robotics could have a breakthrough next year. 👁️ The crunch for Nvidia GPUs is here to stay. It won’t be able to catch up on supply this year. Probably not next year either. 👁️ A new generation of GPUs called Blackwell is coming out, and the performance of Blackwell is off the charts. 👁️ Nvidia’s business is now roughly 70% inference and 30% training, meaning AI is getting into users’ hands. 👁️ Nvidia's also announced new AI chips in a laptop-friendly package. The new RTX 500 and 1000 Ada Generation laptop GPUs are perfect if you want to run crazy generative AI models. 👁️ And, Nvidia crossed $2T in market cap.

A PATTERN OF POSITIONING -- Nvidia CEO, Jensen Huang has made a pattern of positioning Nvidia in front of every big tech trend. • In 2012 a small group of researchers released a groundbreaking image recognition system, called AlexNet, that used GPUs, instead of CPUs, to crunch its code and launched a new era of deep learning. Huang promptly directed the company to chase AI full-steam. • When, in 2017, Google released the novel neural network architecture known as a transformer—the T in ChatGPT—and ignited the current AI gold rush, Nvidia was in a perfect position to start selling its AI-focused GPUs to hungry tech companies. • Nvidia now accounts for more than 70 percent of sales in the AI chip market. Its revenue for the last quarter of 2023 was $22 billion—up 265% from the year prior. And its stock price has risen 231% in the last year. Huang is either uncannily good at what he does or ridiculously lucky—or both!—and everyone wants to know how he does it. Tech companies can’t get enough of this tech company. Earnings are off the charts. WIRED probes the mind of its CEO, Jensen Huang. 👁️ Nvidia is building a new type of data centre called AI factory. Every company—biotech, self-driving, manufacturing, etc will need an AI factory. 👁️ Jensen is looking forward to foundational robotics and state space models. According to him, foundational robotics could have a breakthrough next year. 👁️ The crunch for Nvidia GPUs is here to stay. It won’t be able to catch up on supply this year. Probably not next year either. 👁️ A new generation of GPUs called Blackwell is coming out, and the performance of Blackwell is off the charts. 👁️ Nvidia’s business is now roughly 70% inference and 30% training, meaning AI is getting into users’ hands. 👁️ Nvidia's also announced new AI chips in a laptop-friendly package. The new RTX 500 and 1000 Ada Generation laptop GPUs are perfect if you want to run crazy generative AI models. 👁️ And, Nvidia crossed $2T in market cap.

🚀 NVIDIA: The Force Behind the AI Revolution 🚀 NVIDIA's remarkably revolutionary journey in AI all started with a visionary: Jensen Huang 🔹 2006: NVIDIA launched CUDA 🖥️, a platform that allowed GPUs to handle complex computations—laying the foundation for AI acceleration. 🔹 2010: Researchers used NVIDIA GPUs 🧠 to dramatically speed up deep learning training—a pivotal moment in AI’s evolution. 🔹 2012: NVIDIA GPUs powered AlexNet, which dominated the ImageNet competition 🏆. This was the moment when GPUs became synonymous with AI progress. 🔹 2015: Enter the Tesla K80 GPU, designed specifically for AI ⚡, making AI more accessible and accelerating development. 🔹 2016: The release of Pascal-based GPUs 🚀 unleashed unprecedented computational power, allowing AI to scale like never before. 🔹 2017: The introduction of the Volta architecture and Tensor Cores 🤖 revolutionized AI, dramatically improving model training and inference speeds. 🔹 2018: The DGX systems became indispensable across industries 🌍, giving organizations the horsepower to push the boundaries of AI innovation. 🔹 2020: Even ChatGPT relied on NVIDIA A100 GPUs 💡, demonstrating that modern AI is built on the backbone of NVIDIA’s technology. 🔹 2023: With the launch of Hopper architecture and Superchips 🛠️, NVIDIA propelled AI research to new heights, enabling cutting-edge generative models. 🔹 2024: Now, the Blackwell architecture is setting the stage for AI 4.0 🧑💻, enabling faster and more efficient training of advanced models like ChatGPT. Jensen Huang and NVIDIA didn’t just participate in AI’s development—they built it. Without their relentless innovation, AI today would be unrecognizable. 🌟 How different do you think AI would be today without NVIDIA? 👇 Let’s discuss in the comments! #AI #Nvidia Jensen Huang #ChatGPT OpenAI #Innovation #DeepLearning #TechTransformation #FutureOfAI

🚀 Nvidia, the powerhouse of chipmaking, is trying to use AI in order to speed up 🏃♂️ the way GPUs are designed. Introducing ChipNeMo, Nvidia's custom AI model, which is a great example of how to use AI to optimise the intricate production process 🏭 . (https://lnkd.in/d4yFRQej) 🤖 Leveraging the power of AI, ChipNeMo is transforming the complex task of chip design into a more efficient and collaborative process. Imagine a tool that not only assists with technical queries but also mentors the next generation of engineers. That's the promise of Nvidia's AI-driven approach. Google's DeepMind and software giant Synopsys are also in the race, leveraging AI to streamline semiconductor design. 🔍 #Nvidia #AI #ChipNeMo #Innovation #TechTrends #FutureOfComputing #AIForGood

🚀 NVIDIA: The Force Behind the AI Revolution 🚀 NVIDIA's remarkably revolutionary journey in AI all started with a visionary: Jensen Huang 🔹 2006: NVIDIA launched CUDA 🖥️, a platform that allowed GPUs to handle complex computations—laying the foundation for AI acceleration. 🔹 2010: Researchers used NVIDIA GPUs 🧠 to dramatically speed up deep learning training—a pivotal moment in AI’s evolution. 🔹 2012: NVIDIA GPUs powered AlexNet, which dominated the ImageNet competition 🏆. This was the moment when GPUs became synonymous with AI progress. 🔹 2015: Enter the Tesla K80 GPU, designed specifically for AI ⚡, making AI more accessible and accelerating development. 🔹 2016: The release of Pascal-based GPUs 🚀 unleashed unprecedented computational power, allowing AI to scale like never before. 🔹 2017: The introduction of the Volta architecture and Tensor Cores 🤖 revolutionized AI, dramatically improving model training and inference speeds. 🔹 2018: The DGX systems became indispensable across industries 🌍, giving organizations the horsepower to push the boundaries of AI innovation. 🔹 2020: Even ChatGPT relied on NVIDIA A100 GPUs 💡, demonstrating that modern AI is built on the backbone of NVIDIA’s technology. 🔹 2023: With the launch of Hopper architecture and Superchips 🛠️, NVIDIA propelled AI research to new heights, enabling cutting-edge generative models. 🔹 2024: Now, the Blackwell architecture is setting the stage for AI 4.0 🧑💻, enabling faster and more efficient training of advanced models like ChatGPT. Jensen Huang and NVIDIA didn’t just participate in AI’s development—they built it. Without their relentless innovation, AI today would be unrecognizable. 🌟 How different do you think AI would be today without NVIDIA? 👇 Let’s discuss in the comments! #AI #Nvidia Jensen Huang #ChatGPT OpenAI #Innovation #DeepLearning #TechTransformation #FutureOfAI

Navigating AI's Chip Wars: Sharon Zhou's AMD Strategy Challenges Nvidia Dominance Sharon Zhou, CEO of Lamini AI, stands out in the AI industry for her unconventional approach to technology procurement, diverging from the trend of reliance on Nvidia's GPUs. While many tech leaders scrambled for Nvidia chips, Zhou opted for rival AMD's GPUs to power her startup forward. Leveraging her extensive academic and professional background, Zhou spearheaded Lamini AI's platform, offering enterprises the ability to create customized large language models with ease. Despite the industry's fixation on Nvidia's chips, Zhou's decision to utilize AMD's GPUs proved strategic, ensuring consistent supply and performance while sidestepping the supply constraints plaguing Nvidia. Backed by her cofounder Greg Diamos's expertise and AMD's promising advancements, Zhou confidently navigates the competitive landscape, exemplifying how when one door closes, there's always a window of opportunity. #Nvidia #AI #AMD #Nvidia #TechLeadership #GPU #LLM #ArtificialIntelligence https://lnkd.in/gECeF3rH

Nvidia's latest release, the Blackwell B200, is more than just a chip—it's a leap toward a future where AI is faster, smarter, and more accessible. Boasting 208 billion transistors, this chip is designed to cut the costs and energy needed for AI tasks dramatically. It's a game-changer for businesses big and small, signaling a shift to more efficient technology that promises to streamline operations like running AI platforms. With the introduction of the GB200, Nvidia doubles down on power by pairing two B200 chips with a Grace CPU, delivering performance that could reshape the AI landscape. This advancement isn't merely about speed; it's about making cutting-edge AI advancements available to more people and businesses. Nvidia's move is a nod to the future, where AI is not a high-end luxury but a fundamental part of our digital lives. https://lnkd.in/ga7WM4Tq

NVIDIA UNVEILS BLACKWELL, THE AI SUPERCHIP. Blackwell, the latest GPU that is more than just an improvement, but a significant step toward AI superlearning. But there is more ↓ This processor is 10-100x quicker than its predecessors, allowing AI models to learn and adapt at previously unheard-of speeds. Here is what the major publication saying about the topic: • Verge: Nvidia’s must-have H100 AI chip made it a multitrillion-dollar company, one that may be worth more than Alphabet and Amazon, and competitors have been fighting to catch up. But perhaps Nvidia is about to extend its lead — with the new Blackwell B200 GPU and GB200 “superchip.” • Nvidia blog: Powering a new era of computing, NVIDIA today announced that the NVIDIA Blackwell platform has arrived — enabling organizations everywhere to build and run real-time generative AI on trillion-parameter large language models at up to 25x less cost and energy consumption than its predecessor. • CNBC: Nvidia’s next-generation graphics processor for artificial intelligence, called Blackwell, will cost between $30,000 and $40,000 per unit, CEO Jensen Huang told CNBC’s Jim Cramer. • Bloomberg: The chip promises to extend Nvidia’s lead on rivals at a time when major businesses and even nations are making AI development a priority. After riding Blackwell’s predecessor, Hopper, to surpass a valuation of more than $2 trillion, Nvidia is setting high expectations with its latest product. Let's see Chief Executive Officer Jensen Huang on the stage.

🚀 Groq vs. NVIDIA: A New Era in AI Hardware 🚀 As the demand for AI processing power skyrockets, the competition between Groq and NVIDIA is heating up. Both companies are pushing the boundaries of what's possible, but they take different approaches to achieve their goals. 🔍 Performance Metrics Groq's Language Processing Units (LPUs) are making waves with their ability to process 500 tokens per second at a cost of $20,000. In contrast, NVIDIA's A100 GPU handles only 30 tokens per second for $10,000. This highlights Groq's edge in speed and efficiency for specific AI tasks. ⚡ Architectural Differences Groq’s architecture focuses on simplicity and efficiency, allowing for high compute density and reduced latency. Meanwhile, NVIDIA’s GPUs offer versatility across various applications but can lead to higher power consumption due to their complexity. 💰 Cost Implications While Groq’s LPUs may have a higher upfront cost, their lower power consumption (averaging 240W) can lead to significant long-term savings compared to NVIDIA's A100 GPU (up to 400W). 🌟 Market Positioning NVIDIA remains a dominant player with an established ecosystem, but Groq is emerging as a disruptive force, recently securing $640 million in funding to scale operations and innovate further. In summary, Groq excels in high-speed inference tasks, while NVIDIA continues to lead with a broader range of applications. As we navigate this evolving landscape, it's exciting to see how these two companies will shape the future of AI technology! #AI #Hardware #Innovation #Groq #NVIDIA #TechTrends #MachineLearning

Nvidia Hardware Is Eating the World In a WIRED article that reads more like a sci-fi adventure, Lauren Goode gives us a peek into Nvidia's empire, where CEO Jensen Huang is the mastermind wizard behind the curtain. With a fashion sense stuck in a rock concert and a campus that screams "geometry is cool," Huang is leading Nvidia from the front, turning AI dreams into reality faster than a self-driving car on an open highway. Imagine Nvidia as the cool kid on the tech block, once known for making your video games look pretty, now dominating the AI chip market like it's playing a strategy game on "easy" mode. With a market share of 70% and a valuation nearing $2 trillion, Nvidia under Huang is like a tech juggernaut on a caffeine high, unfazed by competition or regulatory hurdles. Huang, with his disdain for Mondays and knack for tech wizardry, has Nvidia setting trends in AI and computing. It's as if they're not just playing chess; they're three moves ahead in 4D chess. As Huang steers Nvidia through the tech landscape, he's not just aiming for success; he's scripting the future of technology, all while pondering the eternal question: How to make Mondays more exciting. https://lnkd.in/dEdQPuBC