Загрузка видео...

Не удалось загрузить видео

На главную

🚨NVIDIA UNVEILS 72-GPU SUPERCHIP SET TO REVOLUTIONIZE AI Jensen Huang introduced the GB200 NVL72, a data center powerhouse packing 72 Blackwell GPUs, 1.4 exaFLOPS of compute, and 130 trillion transistors. This "superchip" enables test-time scaling, a groundbreaking approach that lets AI reason in real time, mimicking human thought. Huang...

MarioNawfal's profile picture

Mario Nawfal

2,254,723 subscribers

300,346 просмотров • 1 год назад •via X (Twitter)

Наука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 11

Фото профиля Major
Major1 год назад

Can I use it to mine bitcoin?

Фото профиля NetMind.AI
NetMind.AI2 лет назад

Get access to a wide range of GPUs like H100, A100, 4090, 3090 and save over 90% at NetMind Power. Rent Now!

Фото профиля Walker Seattle Ranger
Walker Seattle Ranger1 год назад

Does it run Roblox?

Фото профиля N Hanh
N Hanh1 год назад

you are scammer

Фото профиля NathGraty🕯️
NathGraty🕯️1 год назад

Don't think, just $Stupid and chill CA: 4zUDFduzd6dWisjkrS7d7VSEw3EdrV3t7KkYPznZpump

Фото профиля Sweden4Trump
Sweden4Trump1 год назад

wonder if good to start inverst nvidia shares

Фото профиля Artur
Artur1 год назад

Moore’s Law isn’t dead .. it just planned to moved into a really big house

Фото профиля Mazzagatti
Mazzagatti1 год назад

Bros got a captain America shield with 72 4090’s on it

Фото профиля Kacee Allen
Kacee Allen1 год назад

AI is going to be a major boost for all economies.

Фото профиля AI IA
AI IA1 год назад

Y’all don’t know

Фото профиля Liberty Watch
Liberty Watch1 год назад

Wild stuff

Похожие видео

Open models are accelerating development and deployment across agentic AI, physical AI, and a growing range of industries. ✨ “We have billions of dollars of supercomputers in operation, so we can develop our open models,” said NVIDIA CEO Jensen Huang in a recent keynote. “That work is starting to attract attention across industries worldwide, as we build frontier AI models in many different domains.” This includes leading open models from NVIDIA, including: ⚙️ NVIDIA Nemotron for agentic AI ⚙️ NVIDIA Cosmos and GR00T for physical AI ⚙️ NVIDIA Alpamayo for autonomous vehicles ⚙️ NVIDIA Clara for biomedical AI Learn more about open source and AI ➡️
0:20

Open models are accelerating development and deployment across agentic AI, physical AI, and a growing range of industries. ✨ “We have billions of dollars of supercomputers in operation, so we can develop our open models,” said NVIDIA CEO Jensen Huang in a recent keynote. “That work is starting to attract attention across industries worldwide, as we build frontier AI models in many different domains.” This includes leading open models from NVIDIA, including: ⚙️ NVIDIA Nemotron for agentic AI ⚙️ NVIDIA Cosmos and GR00T for physical AI ⚙️ NVIDIA Alpamayo for autonomous vehicles ⚙️ NVIDIA Clara for biomedical AI Learn more about open source and AI ➡️

NVIDIA AI

19,296 просмотров • 6 месяцев назад

"This is the time you want to be first!" 👏👏👏 Jensen Huang, CEO of NVIDIA, joins Michael Dell 🇺🇸 for a #DellTechWorld conversation, emphasizing the importance of early adoption in the age of AI innovation. Take hold of your AI opportunity now – read about the Dell AI Factory with NVIDIA:
0:20

"This is the time you want to be first!" 👏👏👏 Jensen Huang, CEO of NVIDIA, joins Michael Dell 🇺🇸 for a #DellTechWorld conversation, emphasizing the importance of early adoption in the age of AI innovation. Take hold of your AI opportunity now – read about the Dell AI Factory with NVIDIA:

Dell Technologies

19,339,970 просмотров • 1 год назад

Nvidia $NVDA is at roughly a $160B run rate for the data center. In an interview with Caroline Hyde from Bloomberg Technology, I discuss the path to a $300B run rate plus the constraints that Nvidia and the broader AI market face as we approach future generations of GPUs.
5:00

Nvidia $NVDA is at roughly a $160B run rate for the data center. In an interview with Caroline Hyde from Bloomberg Technology, I discuss the path to a $300B run rate plus the constraints that Nvidia and the broader AI market face as we approach future generations of GPUs.

Beth Kindig

905,406 просмотров • 11 месяцев назад

Student to Rahul Gandhi : How the education system should change in response to the rise of Artificial Intelligence ?? Rahul Gandhi : AI means Production and Manufacturing.🤡 Meanwhile, the CEO of NVIDIA applauding Modi jis foresight in requesting him to address his cabinet about AI, in 2020. (The CEO of NVIDIA is Jensen Huang. He is the founder, president, and chief executive officer of the American technology company NVIDIA Corporation, a leading maker of graphics processing units (GPUs) and AI computing hardware.)
1:15

Student to Rahul Gandhi : How the education system should change in response to the rise of Artificial Intelligence ?? Rahul Gandhi : AI means Production and Manufacturing.🤡 Meanwhile, the CEO of NVIDIA applauding Modi jis foresight in requesting him to address his cabinet about AI, in 2020. (The CEO of NVIDIA is Jensen Huang. He is the founder, president, and chief executive officer of the American technology company NVIDIA Corporation, a leading maker of graphics processing units (GPUs) and AI computing hardware.)

Dr Poornima 🇮🇳

22,206 просмотров • 5 месяцев назад

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️
0:45

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

NVIDIA AI

16,053 просмотров • 6 месяцев назад

Physical AI is the biggest opportunity most people are sleepwalking past. "Physical AI is a $50 trillion market." - Jensen at The All-In Podcast Robots. Autonomous vehicles. Embodied AI. The infrastructure to power all of it. Nvidia isn't just selling GPUs anymore. They're building the OS for the physical AI era.
5:50

Physical AI is the biggest opportunity most people are sleepwalking past. "Physical AI is a $50 trillion market." - Jensen at The All-In Podcast Robots. Autonomous vehicles. Embodied AI. The infrastructure to power all of it. Nvidia isn't just selling GPUs anymore. They're building the OS for the physical AI era.

Shruti

51,545 просмотров • 4 месяцев назад

Telecom infrastructure is becoming a distributed AI compute layer, unlocking smarter cities and more efficient operations. NVIDIA AI-RAN and NVIDIA Metropolis Blueprint for video search and summarization enable AI agents to run continuously at the edge. These agents analyze real-time data streams from connected sensors over 5G to power live monitoring and infrastructure inspection. ▶️ Watch the full video to learn more:
0:44

Telecom infrastructure is becoming a distributed AI compute layer, unlocking smarter cities and more efficient operations. NVIDIA AI-RAN and NVIDIA Metropolis Blueprint for video search and summarization enable AI agents to run continuously at the edge. These agents analyze real-time data streams from connected sensors over 5G to power live monitoring and infrastructure inspection. ▶️ Watch the full video to learn more:

NVIDIA AI

11,078 просмотров • 3 месяцев назад

The real story behind NVIDIA CEO Jensen Huang is not overnight success. He spent decades betting on a revolutionary way to compute that few believed in, building today’s lead in AI and accelerated computing. In this CNBC segment, “The Thinking Machine” author Stephen Witt captures how long-term vision, grit, and relentless engineering at NVIDIA built the foundation for today’s AI era.
12:41

The real story behind NVIDIA CEO Jensen Huang is not overnight success. He spent decades betting on a revolutionary way to compute that few believed in, building today’s lead in AI and accelerated computing. In this CNBC segment, “The Thinking Machine” author Stephen Witt captures how long-term vision, grit, and relentless engineering at NVIDIA built the foundation for today’s AI era.

NVIDIA

39,128 просмотров • 7 месяцев назад

AI IS ABOUT TO RESET THE WORLD’S INDUSTRIES 🤯 NVIDIA CEO Jensen Huang says we’re entering the largest industrial buildout in human history driven by AI. Entire sectors are being rebuilt around AI infrastructure: 🤖 AI factories and chip plants 🤖 Robotics across $50T manufacturing 🤖 AI-powered telecom networks 🤖 Agentic retail and customer service 🤖 Real-time AI in media, gaming, and live video 🤖 Quantum + GPU hybrid systems Even telecom towers -- the backbone of the internet -- could soon become AI compute platforms at the edge. The message from NVIDIA’s GTC stage was clear: The next wave of AI isn’t just software -- it’s a global infrastructure buildout.
2:11

AI IS ABOUT TO RESET THE WORLD’S INDUSTRIES 🤯 NVIDIA CEO Jensen Huang says we’re entering the largest industrial buildout in human history driven by AI. Entire sectors are being rebuilt around AI infrastructure: 🤖 AI factories and chip plants 🤖 Robotics across $50T manufacturing 🤖 AI-powered telecom networks 🤖 Agentic retail and customer service 🤖 Real-time AI in media, gaming, and live video 🤖 Quantum + GPU hybrid systems Even telecom towers -- the backbone of the internet -- could soon become AI compute platforms at the edge. The message from NVIDIA’s GTC stage was clear: The next wave of AI isn’t just software -- it’s a global infrastructure buildout.

CryptosRus

28,976 просмотров • 4 месяцев назад

NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.
1:34

NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.

Ask Perplexity

64,013 просмотров • 9 месяцев назад

Nvidia CEO Jensen Huang predicts $1 trillion in annual spend on AI infrastructure by the end of the decade. This is the biggest boom in history. With the Trump administration enabling easier permitting and new power generation, it’s time to build!
0:38

Nvidia CEO Jensen Huang predicts $1 trillion in annual spend on AI infrastructure by the end of the decade. This is the biggest boom in history. With the Trump administration enabling easier permitting and new power generation, it’s time to build!

David Sacks

431,427 просмотров • 1 год назад

NVIDIA CEO Jensen Huang says one scaling law multiplies AI faster than NVIDIA can hire engineers. Most people know three AI scaling laws. Pre-training. Post-training. Test-time. Each one multiplies intelligence by throwing more compute at a different stage. Jensen Huang says there's a fourth and it's the one that will dominate... Agentic scaling law. "During test time, that agentic system goes off and does research, bangs on databases, uses tools," Huang says. "And one of the most important things it does is spawn off a whole bunch of sub-agents." That's the multiplier. One AI worker can become a team. Then a department. Then a company. "It's so much easier to scale NVIDIA by hiring more employees than it is to scale myself," Huang says. Now imagine scaling without a payroll constraint. "The agentic scaling law — it's kind of like multiplying AI," Huang says. "We could spin off agents as fast as you want to spin off agents." Each agent spins off sub-agents. Each sub-agent spins off more. The compute requirement compounds inside a single query. And every agent generates new data, new experiences, new edge cases. "Wow, this is really good. We ought to memorize this," Huang says. "That data set comes back to pre-training." The four scaling laws don't compete. They feed each other. Agentic systems produce data, which feeds pre-training, which smartens the base model, which enables better agents, which produce more data. A flywheel that compounds forever. The companies pricing in three scaling laws are mispricing the fourth. The fourth eats the other three for lunch. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast
0:55

NVIDIA CEO Jensen Huang says one scaling law multiplies AI faster than NVIDIA can hire engineers. Most people know three AI scaling laws. Pre-training. Post-training. Test-time. Each one multiplies intelligence by throwing more compute at a different stage. Jensen Huang says there's a fourth and it's the one that will dominate... Agentic scaling law. "During test time, that agentic system goes off and does research, bangs on databases, uses tools," Huang says. "And one of the most important things it does is spawn off a whole bunch of sub-agents." That's the multiplier. One AI worker can become a team. Then a department. Then a company. "It's so much easier to scale NVIDIA by hiring more employees than it is to scale myself," Huang says. Now imagine scaling without a payroll constraint. "The agentic scaling law — it's kind of like multiplying AI," Huang says. "We could spin off agents as fast as you want to spin off agents." Each agent spins off sub-agents. Each sub-agent spins off more. The compute requirement compounds inside a single query. And every agent generates new data, new experiences, new edge cases. "Wow, this is really good. We ought to memorize this," Huang says. "That data set comes back to pre-training." The four scaling laws don't compete. They feed each other. Agentic systems produce data, which feeds pre-training, which smartens the base model, which enables better agents, which produce more data. A flywheel that compounds forever. The companies pricing in three scaling laws are mispricing the fourth. The fourth eats the other three for lunch. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

GeniusThinking

92,806 просмотров • 2 месяцев назад

Nvidia said the revenue opportunity for its AI chips may reach at least $1 trillion through 2027, as the company outlined a strategy to compete more aggressively in the fast-growing market for running AI systems in real time
1:59

Nvidia said the revenue opportunity for its AI chips may reach at least $1 trillion through 2027, as the company outlined a strategy to compete more aggressively in the fast-growing market for running AI systems in real time

Reuters

42,530 просмотров • 4 месяцев назад

🇸🇦 SAUDI ARABIA GOES SUPERCOMPUTER: NVIDIA DEAL TO BUILD AI MEGASCALE Saudi Arabia partnership with Nvidia to build one of the world’s largest AI infrastructures is a monumental step in the proliferation of Artificial Intelligence. The Kingdom will deploy 18,000 Nvidia GB300 superchips in Phase 1, aiming for 500 megawatts of compute power within five years. This move will undoubtedly position Saudi Arabia as a regional AI hub amid a global race for computing supremacy. It will support sovereign AI models and digital twin simulations across industries. As Vision 2030 shifts the Kingdom beyond oil, AI is now its new strategic resource—and it's going all in. Source: AI Magazine
2:07

🇸🇦 SAUDI ARABIA GOES SUPERCOMPUTER: NVIDIA DEAL TO BUILD AI MEGASCALE Saudi Arabia partnership with Nvidia to build one of the world’s largest AI infrastructures is a monumental step in the proliferation of Artificial Intelligence. The Kingdom will deploy 18,000 Nvidia GB300 superchips in Phase 1, aiming for 500 megawatts of compute power within five years. This move will undoubtedly position Saudi Arabia as a regional AI hub amid a global race for computing supremacy. It will support sovereign AI models and digital twin simulations across industries. As Vision 2030 shifts the Kingdom beyond oil, AI is now its new strategic resource—and it's going all in. Source: AI Magazine

Mario Nawfal

188,830 просмотров • 1 год назад

Jensen Huang just doubled NVIDIA's demand forecast to $1 Trillion through 2027 🤯 Then spent two hours explaining why that number is conservative… Here's everything today from GTC: - NemoClaw: NVIDIA's open-source enterprise AI agent stack built around OpenClaw. Jensen called OpenClaw "the operating system for personal AI" and said every company needs a strategy for it. - Space-1: NVIDIA is putting Vera Rubin data centers in orbit. Not a concept. An actual system being designed for space deployment right now. - DLSS 5: 3D-guided neural rendering that blends raw graphics with generative AI. Jensen called it the future of real-time rendering. - AWS: Deploying 1 million+ NVIDIA GPUs starting this year. Azure was the first hyperscaler to power up Vera Rubin. - Vera Rubin: NVIDIA's next-gen AI supercomputer. 10x more performance per watt than Blackwell, 700 million tokens per second, shipping later this year. - Groq 3 LPU: First chip from NVIDIA's $20B Groq acquisition. A purpose-built inference accelerator that ships Q3. NVIDIA now owns training AND inference. -Feynman: The architecture after Rubin, coming 2028. New GPU, new LPU, new CPU. NVIDIA is on a 12-month chip cadence and the treadmill never stops. - Autonomous driving: BYD, Hyundai, Nissan, and Geely building Level 4 vehicles on NVIDIA. Uber deploying NVIDIA-powered robotaxis across 28 cities by 2028. The man doubled his demand forecast to a trillion dollars, announced data centers in space, and closed the show with a robot singing country music. This is NVIDIA's world. Everyone else is just renting compute in it.
0:49

Jensen Huang just doubled NVIDIA's demand forecast to $1 Trillion through 2027 🤯 Then spent two hours explaining why that number is conservative… Here's everything today from GTC: - NemoClaw: NVIDIA's open-source enterprise AI agent stack built around OpenClaw. Jensen called OpenClaw "the operating system for personal AI" and said every company needs a strategy for it. - Space-1: NVIDIA is putting Vera Rubin data centers in orbit. Not a concept. An actual system being designed for space deployment right now. - DLSS 5: 3D-guided neural rendering that blends raw graphics with generative AI. Jensen called it the future of real-time rendering. - AWS: Deploying 1 million+ NVIDIA GPUs starting this year. Azure was the first hyperscaler to power up Vera Rubin. - Vera Rubin: NVIDIA's next-gen AI supercomputer. 10x more performance per watt than Blackwell, 700 million tokens per second, shipping later this year. - Groq 3 LPU: First chip from NVIDIA's $20B Groq acquisition. A purpose-built inference accelerator that ships Q3. NVIDIA now owns training AND inference. -Feynman: The architecture after Rubin, coming 2028. New GPU, new LPU, new CPU. NVIDIA is on a 12-month chip cadence and the treadmill never stops. - Autonomous driving: BYD, Hyundai, Nissan, and Geely building Level 4 vehicles on NVIDIA. Uber deploying NVIDIA-powered robotaxis across 28 cities by 2028. The man doubled his demand forecast to a trillion dollars, announced data centers in space, and closed the show with a robot singing country music. This is NVIDIA's world. Everyone else is just renting compute in it.

Josh Kale

45,875 просмотров • 4 месяцев назад

Last night, Jensen Huang of NVIDIA gave his very first live keynote in 4-years. The most show-stopping moment from the event was when he showed off the real-time AI in video games. A human speaks, the NPC responds, in real time and the dialogue was generated with AI on the fly.
1:41

Last night, Jensen Huang of NVIDIA gave his very first live keynote in 4-years. The most show-stopping moment from the event was when he showed off the real-time AI in video games. A human speaks, the NPC responds, in real time and the dialogue was generated with AI on the fly.

Matt Wolfe

5,387,304 просмотров • 3 лет назад

🚨 🇰🇷 Jensen Huang hints at deeper NVIDIA-LG collaboration in robotics and AI data centers NVIDIA CEO Jensen Huang visited LG Twin Towers on June 8 and emphasized the company’s growing partnership with LG after meeting with LG Chairman Koo Kwang-mo and other executives. Huang said LG is a world-class company across many important future technologies and noted that NVIDIA and LG are working together on many areas. He highlighted robotics as one of the most important fields, describing it as the fusion of electronics, mechanical systems, and artificial intelligence. He said NVIDIA is working with LG on motor technology and mechanical systems, which could support future humanoid robotics. Huang also pointed to next-generation data centers, saying future facilities will scale to gigawatt levels and require advanced cooling, power, and infrastructure technologies. He added that there will be “many more announcements” in the near future, suggesting NVIDIA and LG’s cooperation in AI, robotics, and data centers could become more concrete soon.
2:03

🚨 🇰🇷 Jensen Huang hints at deeper NVIDIA-LG collaboration in robotics and AI data centers NVIDIA CEO Jensen Huang visited LG Twin Towers on June 8 and emphasized the company’s growing partnership with LG after meeting with LG Chairman Koo Kwang-mo and other executives. Huang said LG is a world-class company across many important future technologies and noted that NVIDIA and LG are working together on many areas. He highlighted robotics as one of the most important fields, describing it as the fusion of electronics, mechanical systems, and artificial intelligence. He said NVIDIA is working with LG on motor technology and mechanical systems, which could support future humanoid robotics. Huang also pointed to next-generation data centers, saying future facilities will scale to gigawatt levels and require advanced cooling, power, and infrastructure technologies. He added that there will be “many more announcements” in the near future, suggesting NVIDIA and LG’s cooperation in AI, robotics, and data centers could become more concrete soon.

TeslaZoa

35,216 просмотров • 1 месяц назад

Jensen Huang plays out what would happen if every company augmented their employees with AI the way that NVIDIA does: "Human intelligence represents 55% - 65% of the world's GDP. Let's call it $50 trillion. Suppose I were to hire a $100,000 employee and I augmented that $100,000 employee with a $10,000 AI. And that $10,000 AI, as a result, made the $100,000 employee 2x more productive, 3x more productive.. Would I do it? Heartbeat. I'm doing it across every single person in our company right now. ...Now, apply the NVIDIA story to the world's GDP. What's likely to happen is that that $50 trillion is augmented by, let's pick a number, $10 trillion, that $10 trillion needs to run on a machine." — Jensen Huang with Brad Gerstner, Clark Tang (and a special appearance by Dr. Parik Patel, BA, CFA, ACCA Esq.)
2:10

Jensen Huang plays out what would happen if every company augmented their employees with AI the way that NVIDIA does: "Human intelligence represents 55% - 65% of the world's GDP. Let's call it $50 trillion. Suppose I were to hire a $100,000 employee and I augmented that $100,000 employee with a $10,000 AI. And that $10,000 AI, as a result, made the $100,000 employee 2x more productive, 3x more productive.. Would I do it? Heartbeat. I'm doing it across every single person in our company right now. ...Now, apply the NVIDIA story to the world's GDP. What's likely to happen is that that $50 trillion is augmented by, let's pick a number, $10 trillion, that $10 trillion needs to run on a machine." — Jensen Huang with Brad Gerstner, Clark Tang (and a special appearance by Dr. Parik Patel, BA, CFA, ACCA Esq.)

bg2clips

11,603 просмотров • 10 месяцев назад

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries
0:59

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

Daniel

151,698 просмотров • 7 месяцев назад