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AI demand is exploding, but scaling it isn’t just about more compute. At NVIDIA GTC, Samsung Semiconductor US President Paul Cho shares where the real innovation is happening. Samsung’s HBM4 shows that the next phase of AI performance will be driven by memory, integration, and system design. And the... show more

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Everyone’s still talking about GPUs. Meanwhile, memory is now the real constraint for AI at scale. From GTC, Patrick Moorhead & Daniel Newman are joined by Paul Cho & Indong Kim from Samsung Semiconductor US to discuss why bandwidth, not compute, is starting to define AI performance.

Everyone’s still talking about GPUs. Meanwhile, memory is now the real constraint for AI at scale. From GTC, Patrick Moorhead & Daniel Newman are joined by Paul Cho & Indong Kim from Samsung Semiconductor US to discuss why bandwidth, not compute, is starting to define AI performance.

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AI scaling is running into some hard limits. Primarily memory, bandwidth, and power. Samsung Semiconductor US President Paul Cho joined Patrick Moorhead and Daniel Newman to unpack how the industry is responding at The Six Five Summit: AI Unleashed 2026. Watch on demand:

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$MU $SKHY $DRAM Holy Shit $AMD's Lisa Su just spent two full minutes at the 2026 AI keynote raving about HBM4 capacity as the thing that separates Helios from the competition. Not compute. Memory. "When you compare Helios to the competition, we're delivering 15% more compute, 50% more HBM4 memory capacity and memory bandwidth, and 50% more scale-out bandwidth." Notice which number is bigger. "What that means is that every Helios can deliver more performance for the largest models, more capacity for longer context, and the bandwidth to scale across thousands of racks.... More than 18,000 CDNA five GPU compute units, over 4,600 Zen 6 CPU cores, and 31 terabytes of HBM4 memory, all in a single rack." "That's what it takes to run agentic AI at scale. Today, I'm excited to announce that Helios is in full production. We have shipments on track to start at the end of the third quarter and ramping into the fourth quarter in the second half of the year. I can tell you, customer demand for Helios is extremely strong, we're extremely proud of the work that we have done across the leading AI labs to adopt Helios."

$MU $SKHY $DRAM Holy Shit $AMD's Lisa Su just spent two full minutes at the 2026 AI keynote raving about HBM4 capacity as the thing that separates Helios from the competition. Not compute. Memory. "When you compare Helios to the competition, we're delivering 15% more compute, 50% more HBM4 memory capacity and memory bandwidth, and 50% more scale-out bandwidth." Notice which number is bigger. "What that means is that every Helios can deliver more performance for the largest models, more capacity for longer context, and the bandwidth to scale across thousands of racks.... More than 18,000 CDNA five GPU compute units, over 4,600 Zen 6 CPU cores, and 31 terabytes of HBM4 memory, all in a single rack." "That's what it takes to run agentic AI at scale. Today, I'm excited to announce that Helios is in full production. We have shipments on track to start at the end of the third quarter and ramping into the fourth quarter in the second half of the year. I can tell you, customer demand for Helios is extremely strong, we're extremely proud of the work that we have done across the leading AI labs to adopt Helios."

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This is what happens when GTC lands in Taipei. 🇹🇼 Keynote reveals, hands-on demos, real connections, and Jensen fans everywhere. It's not just a conference. It's where the AI era gets built together — and the energy of the community proves it. Join us at our next GTC — grow your AI skills, network, and experience the vibe for yourself.

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Inside XDC NYC for the XDC AI Agents & Blockchain Accelerator with Plug and Play. XDC’s Travis John, Billy Sebell & Sebastian Eelbode share more about the program, alongside leaders from IBM and Goldman Sachs. The next phase of AI will be defined by what can actually be deployed. This cohort is about finding the companies building what comes next — and creating a path to real-world adoption. Meet the cohort: BlockRunAI t54.ai hinkal Synergetics Vishwa AstraSync AI XDC AI Keyban Quote.Trade Rupi bond.credit XDC’s mission is to bring builders and institutions together to turn emerging technology into real-world opportunities. More to come 🤝

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XDC USA

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Kim Jung-ho, the pioneer of HBM, says SK hynix is clearly advantaged on HBM3 today but by HBM4 and HBM5 the performance delta versus Samsung is basically null, and growth will depend on which company's ecosystem grows more. "Looking at things as they stand, the two companies have different ecosystems. Samsung does DRAM, HBM, HBF, foundry and packaging, all of it, so it's a total-solution company, and you can see its growth and expansion potential as larger." "By comparison, SK hynix does HBM and DRAM, but foundry and packaging are done at TSMC, and GPU design at NVIDIA. That's the ecosystem it has. In AI semiconductors, it has a firmly established ecosystem." "In that respect, in HBM SK hynix is advantaged, while Samsung, moving beyond NVIDIA, can take Google, Meta, Apple and others as new partners. That is its advantage." "Today market size and stability are higher for SK hynix, but I see Samsung's expansion potential as greater." "Looking at HBM alone, through HBM3 SK hynix clearly holds the advantage. Part of that is being inside NVIDIA and TSMC's ecosystem. And it is also true that hynix's HBM performance is better." "But at HBM4 and HBM5, I think the performance difference will be almost none. Then the two companies' growth depends on which ecosystem grows more."

Kim Jung-ho, the pioneer of HBM, says SK hynix is clearly advantaged on HBM3 today but by HBM4 and HBM5 the performance delta versus Samsung is basically null, and growth will depend on which company's ecosystem grows more. "Looking at things as they stand, the two companies have different ecosystems. Samsung does DRAM, HBM, HBF, foundry and packaging, all of it, so it's a total-solution company, and you can see its growth and expansion potential as larger." "By comparison, SK hynix does HBM and DRAM, but foundry and packaging are done at TSMC, and GPU design at NVIDIA. That's the ecosystem it has. In AI semiconductors, it has a firmly established ecosystem." "In that respect, in HBM SK hynix is advantaged, while Samsung, moving beyond NVIDIA, can take Google, Meta, Apple and others as new partners. That is its advantage." "Today market size and stability are higher for SK hynix, but I see Samsung's expansion potential as greater." "Looking at HBM alone, through HBM3 SK hynix clearly holds the advantage. Part of that is being inside NVIDIA and TSMC's ecosystem. And it is also true that hynix's HBM performance is better." "But at HBM4 and HBM5, I think the performance difference will be almost none. Then the two companies' growth depends on which ecosystem grows more."

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🚨 SOMETHING VERY STRANGE IS HAPPENING Anthropic will go public in November at a $2T valuation. The biggest IPO in market history. And Wall Street is already lining up the buyers before it happens. I've been trading for more than 15 years and have never seen them build demand for an IPO this aggressively: Anthropic is preparing to raise $100 BILLION. Nvidia is lining up as much as $10 BILLION as an anchor investor. Read that again: The company selling the chips powering the AI boom is about to become one of the biggest buyers of the AI company going public. Before the public even gets in. Why? Because Anthropic does not just create demand for Anthropic. It pulls liquidity from everywhere else: - Retail sells stocks to chase the IPO. - Funds raise cash for allocation. - Institutions rebalance portfolios. - Everyone wants exposure to the biggest AI deal in history. But here is where most people are looking at it wrong. They’re asking: WHAT WILL ANTHROPIC TAKE MONEY FROM? I’m asking: WHERE WILL ALL THAT MONEY GO NEXT? That capital funds more compute: More compute means more chips. More chips = more data centers. More data centers = more electricity, grid infrastructure and raw materials. The chain is simple: ANTHROPIC → CHIPS → DATA CENTERS → POWER → COPPER That is where the opportunity starts. The first phase of the AI boom was about the models: ChatGPT. Claude. Gemini. The next phase is about the physical infrastructure needed to keep them running. Electricity. Power grids. Semiconductors. Data centers. Cooling. Copper. I told you to buy copper months ago. We already locked in BIG profits. And that wasn’t random: AI does not run on prompts. It runs on physical infrastructure. Now look at Nvidia: AI companies spend billions buying Nvidia chips. Nvidia makes billions from that demand. Now Nvidia is preparing to put as much as $10 BILLION BACK into Anthropic. AI money → Nvidia → Anthropic → more compute → more infrastructure A $100B raise does not stop at Anthropic. It works its way through the entire AI supply chain. The easy AI trade was buying the obvious names. The next trade is finding the bottlenecks BEFORE everyone else realizes they are bottlenecks. That is what I’m looking for now. That is where the next opportunity will be. Remember, I’ve been trading markets for over 15 years. I’m already watching where this capital is moving next. When I find the next opportunity worth taking, I’ll post it here like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

Alex Mason 👁△

122,728 görüntüleme • 17 gün önce

AI is shortening production timelines, but Alex 🧩 Scaling Video GenAI says that is only stage one. He predicts AI-native IP will emerge by the end of 2026 and that the next Netflix-scale media company will be AI-first. Hear the Higgsfield AI 🧩 co-founder’s full take:

AI is shortening production timelines, but Alex 🧩 Scaling Video GenAI says that is only stage one. He predicts AI-native IP will emerge by the end of 2026 and that the next Netflix-scale media company will be AI-first. Hear the Higgsfield AI 🧩 co-founder’s full take:

Nebius

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CEO Jensen Huang took the stage at GTC Taipei and redefined the future of AI. He unveiled AI infrastructure running the world's AI factories, autonomous agents that actually do things, physical AI and robotics stepping into the real world, and a brand-new generation of personal computing built for AI from the ground up. If you're curious where AI is actually headed, this keynote is your answer. 📰 Get the recap: ▶️ Watch the full keynote: #NVIDIAGTC

NVIDIA

89,244 görüntüleme • 4 ay önce

AI investment is expanding beyond models and applications into the full stack that supports them. Energy capacity, semiconductor supply, compute infrastructure, and software layers are becoming more connected, influencing how and where AI can be built and deployed. In partnership with NVIDIA, The Futurum Group examines the Five Layers of the AI Cake framework as a way to understand the structure of the AI economy. The market brief outlines how value is distributed across the stack, where bottlenecks are forming, and what these dynamics mean for enterprise strategy, capital allocation, workforce planning, and global competitiveness. ⏯️ Explore the key findings, and use the link to download the full report: 👉 #AIInfrastructure #NVIDIA #Futurum

AI investment is expanding beyond models and applications into the full stack that supports them. Energy capacity, semiconductor supply, compute infrastructure, and software layers are becoming more connected, influencing how and where AI can be built and deployed. In partnership with NVIDIA, The Futurum Group examines the Five Layers of the AI Cake framework as a way to understand the structure of the AI economy. The market brief outlines how value is distributed across the stack, where bottlenecks are forming, and what these dynamics mean for enterprise strategy, capital allocation, workforce planning, and global competitiveness. ⏯️ Explore the key findings, and use the link to download the full report: 👉 #AIInfrastructure #NVIDIA #Futurum

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75,859 görüntüleme • 5 ay önce

This is where it all started. 🚀 Ten years ago at #NVIDIAGTC, the journey of #NVIDIADGX began with our CEO Jensen Huang's announcement of DGX-1, the world’s first AI supercomputer. What started as a single system has since evolved into the universal blueprint for modern AI factories. Relive this momentous beginning and explore where the next decade of AI discovery will take us. Add DGX sessions to your GTC schedule now: #DecadeOfDGX

This is where it all started. 🚀 Ten years ago at #NVIDIAGTC, the journey of #NVIDIADGX began with our CEO Jensen Huang's announcement of DGX-1, the world’s first AI supercomputer. What started as a single system has since evolved into the universal blueprint for modern AI factories. Relive this momentous beginning and explore where the next decade of AI discovery will take us. Add DGX sessions to your GTC schedule now: #DecadeOfDGX

NVIDIA

34,003 görüntüleme • 7 ay önce

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.

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 görüntüleme • 6 ay önce

🎤 Overheard at #CES2026: AI compute is entering the yottascale era. William Myrhang shows how the AMD Helios AI Platform enables AI to scale by the rack and by the data center, not one server at a time. Designed for agentic and large-scale AI workloads, Helios makes yottascale compute practical, deployable, and sustainable. Zettaflops defined the early AI boom. Yottaflops will define what comes next. AMD is helping build the open foundation needed to support this unprecedented scale.

🎤 Overheard at #CES2026: AI compute is entering the yottascale era. William Myrhang shows how the AMD Helios AI Platform enables AI to scale by the rack and by the data center, not one server at a time. Designed for agentic and large-scale AI workloads, Helios makes yottascale compute practical, deployable, and sustainable. Zettaflops defined the early AI boom. Yottaflops will define what comes next. AMD is helping build the open foundation needed to support this unprecedented scale.

AMD

21,014 görüntüleme • 8 ay önce

🚨 NOBODY IS PREPARED FOR WHAT $MU IS ABOUT TO DO NEXT Micron Technologies has been leading the memory sector in this AI rally. That was phase 1... Now we enter phase 2: the re-accumulation stage. But, phase 3 is where it gets fun. There is only 1 place to get memory in America and that's $MU. Therefore it will likely lead the race again. Do not miss this.

🚨 NOBODY IS PREPARED FOR WHAT $MU IS ABOUT TO DO NEXT Micron Technologies has been leading the memory sector in this AI rally. That was phase 1... Now we enter phase 2: the re-accumulation stage. But, phase 3 is where it gets fun. There is only 1 place to get memory in America and that's $MU. Therefore it will likely lead the race again. Do not miss this.

Coinvo Trading

40,772 görüntüleme • 2 ay önce

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

130,756 görüntüleme • 3 ay önce

The next computing shift is already underway — and it’s happening faster than even the people building it expected. Meta CTO Boz joins David George to talk about how the team is rooted in solving real-world problems — and how this new wave of AI is unlike any before, ushering us towards a future beyond the smartphone. Boz says AI isn’t just making things faster or cheaper, it’s making everything better. From interfaces to interaction design, AI is transforming how we build — and how we compute. For more on the future of computing, wearables, and AI-native UX, watch the full conversation with Boz and David:

The next computing shift is already underway — and it’s happening faster than even the people building it expected. Meta CTO Boz joins David George to talk about how the team is rooted in solving real-world problems — and how this new wave of AI is unlike any before, ushering us towards a future beyond the smartphone. Boz says AI isn’t just making things faster or cheaper, it’s making everything better. From interfaces to interaction design, AI is transforming how we build — and how we compute. For more on the future of computing, wearables, and AI-native UX, watch the full conversation with Boz and David:

a16z

17,489 görüntüleme • 1 yıl önce