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$MU $00060 $00530 $TSM Evolution of the AI Memory Hierarchy: HBM, DRAM, & SRAM Overview and Market Analysis The video outlines the strategic shift of modern computing toward a memory-centric architecture driven by the demands of generative AI. It explains that the industry is moving away from a traditional...

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23,901 次观看 • 8 个月前 •via X (Twitter)

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The CEO of $ARM explains where the bottlenecks for AI data centers are right now: - power & power regulation, - $TSM "which is the only game in town" for 3nm and 2nm - HBM memory There is a reason why $TSM, $MU, and SK Hynix are a significant part of my portfolio.
0:23

The CEO of $ARM explains where the bottlenecks for AI data centers are right now: - power & power regulation, - $TSM "which is the only game in town" for 3nm and 2nm - HBM memory There is a reason why $TSM, $MU, and SK Hynix are a significant part of my portfolio.

Rihard Jarc

94,253 次观看 • 1 年前

Micron's Sanjay Mehrotra on memory: "Memory is no longer a component in a system, memory is the strategic infrastructure for AI. It is no longer a commodity, it is a high value. Without memory, you cannot make AI smarter, cannot make it faster, you cannot scale up AI...AI is driving a whole hierarchy of memory requirements from HBM to DRAM to SSDs." Via CNBC $MU $DRAM $EWY $SNDK
0:46

Micron's Sanjay Mehrotra on memory: "Memory is no longer a component in a system, memory is the strategic infrastructure for AI. It is no longer a commodity, it is a high value. Without memory, you cannot make AI smarter, cannot make it faster, you cannot scale up AI...AI is driving a whole hierarchy of memory requirements from HBM to DRAM to SSDs." Via CNBC $MU $DRAM $EWY $SNDK

P Equity Research 📰

50,226 次观看 • 11 天前

Gavin Baker, CIO of Atreides Management made one of the most important and nuanced calls on memory stocks in recent months (Save this). His argument is that based on every memory cycle of the last 25 years, the setup today, prices elevated, sentiment high, supply ramping is textbook time to sell but he adds a critical exception. The one cycle in modern memory history where selling was catastrophically wrong was the mid-1990s, which Baker calls the last true capacity cycle in memory. In that cycle, demand was structurally exploding as the internet era required entirely new computing infrastructure to be built from scratch, and memory had to scale with it in a way that had never happened before. His point is that AI may be that same kind of cycle and not a normal boom bust but a once in a generation capacity buildout where the underlying demand is structural, not cyclical. The reason this argument holds weight is the fundamental shift in what memory is in the AI era. Traditional DRAM was a pure commodity, identical specs, interchangeable suppliers, price determined entirely by supply and demand swings. HBM is the opposite because it is custom engineered to fit a specific customer's chip, co-designed between the memory maker and the GPU designer, with SK Hynix's Vice President literally describing it as shifting from a commodity to a customer-tailored custom business. A single Blackwell Ultra GPU now requires up to 288GB of HBM3E, a 3.6x increase over the H100 and major suppliers like SK Hynix and Micron have already sold out their entire HBM production capacity through the end of the year. Because HBM requires advanced packaging processes like CoWoS that can't be spun up overnight, the bottleneck isn't just wafer capacity but rather runs across the entire manufacturing stack. Bank of America projects the global HBM market grows 58% this year alone to $54.6 billion, and Nomura expects the broader memory sector to nearly double to $445 billion. Long Micron!
0:42

Gavin Baker, CIO of Atreides Management made one of the most important and nuanced calls on memory stocks in recent months (Save this). His argument is that based on every memory cycle of the last 25 years, the setup today, prices elevated, sentiment high, supply ramping is textbook time to sell but he adds a critical exception. The one cycle in modern memory history where selling was catastrophically wrong was the mid-1990s, which Baker calls the last true capacity cycle in memory. In that cycle, demand was structurally exploding as the internet era required entirely new computing infrastructure to be built from scratch, and memory had to scale with it in a way that had never happened before. His point is that AI may be that same kind of cycle and not a normal boom bust but a once in a generation capacity buildout where the underlying demand is structural, not cyclical. The reason this argument holds weight is the fundamental shift in what memory is in the AI era. Traditional DRAM was a pure commodity, identical specs, interchangeable suppliers, price determined entirely by supply and demand swings. HBM is the opposite because it is custom engineered to fit a specific customer's chip, co-designed between the memory maker and the GPU designer, with SK Hynix's Vice President literally describing it as shifting from a commodity to a customer-tailored custom business. A single Blackwell Ultra GPU now requires up to 288GB of HBM3E, a 3.6x increase over the H100 and major suppliers like SK Hynix and Micron have already sold out their entire HBM production capacity through the end of the year. Because HBM requires advanced packaging processes like CoWoS that can't be spun up overnight, the bottleneck isn't just wafer capacity but rather runs across the entire manufacturing stack. Bank of America projects the global HBM market grows 58% this year alone to $54.6 billion, and Nomura expects the broader memory sector to nearly double to $445 billion. Long Micron!

Milk Road AI

260,701 次观看 • 2 个月前

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."
1:43

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."

Fireside Alpha

42,654 次观看 • 1 个月前

$MU hit a $1T valuation today because the market is waking up to the idea that memory may actually become one of the core infrastructure layers of the AI economy. The catalyst was SK Hynix unveiling iHBM which is a new cooling technology for next-gen HBM but the bigger point is that token growth, AI factories, inference demand & next-gen accelerators all require more HBM per system. More HBM per accelerator means more memory dollar content & with only three major HBM suppliers in the world the entire memory complex is getting re-rated.
0:54

$MU hit a $1T valuation today because the market is waking up to the idea that memory may actually become one of the core infrastructure layers of the AI economy. The catalyst was SK Hynix unveiling iHBM which is a new cooling technology for next-gen HBM but the bigger point is that token growth, AI factories, inference demand & next-gen accelerators all require more HBM per system. More HBM per accelerator means more memory dollar content & with only three major HBM suppliers in the world the entire memory complex is getting re-rated.

Shay Boloor

306,107 次观看 • 3 个月前

SK Hynix's Hoshik Kim admits high bandwidth memory "is not the answer" to the memory-wall problem, and SK is working on different solutions to solve the bottleneck. "No, actually, I kind of agree with Pat. So HBM is not the answer." [SK Hynix is admitting that HBM is a bad memory?] "Nothing is a bad memory. It's a good memory, but it's not the final answer. So actually, HBM will not solve the memory-wall problem. Memory-wall problem is an inherent AI problem, which you cannot avoid." "So we try our best to provide higher bandwidth with this memory, with HBM technology even though it has some limitation, as Pat already mentioned, with thermal, IOs, cost, a lot of stuff. "But that's the only thing we got right now to help ease this memory-wall problem, memory-bottleneck problem. But at the same time, we are working in different directions to solve this memory-wall problem with different solutions."
0:51

SK Hynix's Hoshik Kim admits high bandwidth memory "is not the answer" to the memory-wall problem, and SK is working on different solutions to solve the bottleneck. "No, actually, I kind of agree with Pat. So HBM is not the answer." [SK Hynix is admitting that HBM is a bad memory?] "Nothing is a bad memory. It's a good memory, but it's not the final answer. So actually, HBM will not solve the memory-wall problem. Memory-wall problem is an inherent AI problem, which you cannot avoid." "So we try our best to provide higher bandwidth with this memory, with HBM technology even though it has some limitation, as Pat already mentioned, with thermal, IOs, cost, a lot of stuff. "But that's the only thing we got right now to help ease this memory-wall problem, memory-bottleneck problem. But at the same time, we are working in different directions to solve this memory-wall problem with different solutions."

Fireside Alpha

140,571 次观看 • 1 个月前

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.
0:58

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.

Ricardo

594,643 次观看 • 7 个月前

🚨 WHAT IF THE MEMORY SHORTAGE WAS MANUFACTURED? Samsung, SK Hynix, and Micron are being sued for allegedly engineering the DRAM shortage on purpose. The lawsuit claims they used the AI boom as cover to quietly cut normal memory supply. DRAM prices are up 500 to 700%. The damage is now showing up in iPhone and Mac prices. The makers say it's innocent: AI buyers showed up with huge money for HBM. But here's the bigger question. Memory stocks are priced for endless AI demand. If a court rules the scarcity was manufactured, how much of that demand was ever real?
0:37

🚨 WHAT IF THE MEMORY SHORTAGE WAS MANUFACTURED? Samsung, SK Hynix, and Micron are being sued for allegedly engineering the DRAM shortage on purpose. The lawsuit claims they used the AI boom as cover to quietly cut normal memory supply. DRAM prices are up 500 to 700%. The damage is now showing up in iPhone and Mac prices. The makers say it's innocent: AI buyers showed up with huge money for HBM. But here's the bigger question. Memory stocks are priced for endless AI demand. If a court rules the scarcity was manufactured, how much of that demand was ever real?

CryptoGoos

32,615 次观看 • 2 个月前

Speaking of spot on. Gavin Gavin Baker was asked in early Jan'25 his prediction for best performing asset class of the year (as @jason references in latest The All-In Podcast): "I think the companies that make high bandwidth memory. Going back to "we're going to run out of compute," it's actually a pretty shocking stat: high bandwidth memory is a bigger part of NVIDIA's COGS on GPUs than Taiwan Semi is. And today there's two companies that can make it, SK Hynix and Micron. We'll see if Samsung gets their act together. But HBM, it's in NVIDIA GPUs, AMD GPUs, Amazon Trainiums. And particularly in a world of test-time compute and inference being so important, high bandwidth memory is arguably more important than it ever was. And it's been sold out for the last two years. So high bandwidth memory would be my pick."
0:52

Speaking of spot on. Gavin Gavin Baker was asked in early Jan'25 his prediction for best performing asset class of the year (as @jason references in latest The All-In Podcast): "I think the companies that make high bandwidth memory. Going back to "we're going to run out of compute," it's actually a pretty shocking stat: high bandwidth memory is a bigger part of NVIDIA's COGS on GPUs than Taiwan Semi is. And today there's two companies that can make it, SK Hynix and Micron. We'll see if Samsung gets their act together. But HBM, it's in NVIDIA GPUs, AMD GPUs, Amazon Trainiums. And particularly in a world of test-time compute and inference being so important, high bandwidth memory is arguably more important than it ever was. And it's been sold out for the last two years. So high bandwidth memory would be my pick."

Fireside Alpha

66,227 次观看 • 2 个月前

If $MU confirms tomorrow that DRAM, NAND and HBM tightness can last into 2027 or 2028 then memory companies have more reason to keep spending on capacity and advanced manufacturing tools. That matters for $ASML because it sits at the most important equipment layer of the semiconductor supply chain where more advanced memory capacity means more demand for lithography systems.
0:59

If $MU confirms tomorrow that DRAM, NAND and HBM tightness can last into 2027 or 2028 then memory companies have more reason to keep spending on capacity and advanced manufacturing tools. That matters for $ASML because it sits at the most important equipment layer of the semiconductor supply chain where more advanced memory capacity means more demand for lithography systems.

Shay Boloor

431,417 次观看 • 2 个月前

Jensen Huang just said the semiconductor industry needs to grow 5-10x over the next decade and he named the exact bottlenecks investors should be watching (Save this). His argument is that this isn't a normal cyclical boom that will bust like past chip cycles because it's driven by a structural shift, the world needs a whole new intelligence layer of infrastructure on top of energy, internet, and roads and that layer runs entirely on chips. Using WSTS's 2026 industry estimate of $1.5 trillion, a 5-10x expansion would put the semiconductor industry at roughly $7.5 trillion to $15 trillion within ten years. He specifically called out five areas that are already in shortage, memory, storage, optical interconnects, packaging, and TSMC foundry capacity, and said the whole industry is short because this is infrastructure demand, not seasonal demand. Here's who stands to benefit in each of those five bottlenecks he named. Memory and storage is the most direct beneficiary, since Micron is the only major US based DRAM and NAND producer and has been reporting surging prices tied to AI server demand while SK Hynix and Samsung, both foreign, dominate the high bandwidth memory that feeds directly into GPUs like Nvidia's. Optical interconnects benefits companies like Coherent and Lumentum which make the optical transceivers moving data between GPU clusters, alongside Credo Technology, which makes high speed interconnect chips for the same data movement problem inside AI clusters. Packaging is where Nvidia's own chips get bottlenecked, since advanced packaging capacity mostly sits with TSMC, but Amkor Technology and ASE Technology are the independent outsourced packaging players that pick up overflow demand when TSMC can't keep up. Chip equipment makers that supply the tools needed to actually build more capacity across all these bottlenecks also benefit, including ASML for lithography, and Lam Research, KLA Corporation and Applied Materials for etching, deposition, and inspection equipment, since a 5-10x industry expansion requires massive new fab buildout that all runs through these companies first. Milk Road Pro is tracking the entire supply chain bottlenecks, if you want our entire AI trades around this, you can come join us just for a dollar.
1:32

Jensen Huang just said the semiconductor industry needs to grow 5-10x over the next decade and he named the exact bottlenecks investors should be watching (Save this). His argument is that this isn't a normal cyclical boom that will bust like past chip cycles because it's driven by a structural shift, the world needs a whole new intelligence layer of infrastructure on top of energy, internet, and roads and that layer runs entirely on chips. Using WSTS's 2026 industry estimate of $1.5 trillion, a 5-10x expansion would put the semiconductor industry at roughly $7.5 trillion to $15 trillion within ten years. He specifically called out five areas that are already in shortage, memory, storage, optical interconnects, packaging, and TSMC foundry capacity, and said the whole industry is short because this is infrastructure demand, not seasonal demand. Here's who stands to benefit in each of those five bottlenecks he named. Memory and storage is the most direct beneficiary, since Micron is the only major US based DRAM and NAND producer and has been reporting surging prices tied to AI server demand while SK Hynix and Samsung, both foreign, dominate the high bandwidth memory that feeds directly into GPUs like Nvidia's. Optical interconnects benefits companies like Coherent and Lumentum which make the optical transceivers moving data between GPU clusters, alongside Credo Technology, which makes high speed interconnect chips for the same data movement problem inside AI clusters. Packaging is where Nvidia's own chips get bottlenecked, since advanced packaging capacity mostly sits with TSMC, but Amkor Technology and ASE Technology are the independent outsourced packaging players that pick up overflow demand when TSMC can't keep up. Chip equipment makers that supply the tools needed to actually build more capacity across all these bottlenecks also benefit, including ASML for lithography, and Lam Research, KLA Corporation and Applied Materials for etching, deposition, and inspection equipment, since a 5-10x industry expansion requires massive new fab buildout that all runs through these companies first. Milk Road Pro is tracking the entire supply chain bottlenecks, if you want our entire AI trades around this, you can come join us just for a dollar.

Milk Road AI

29,530 次观看 • 1 个月前

What if the AI bubble was built on an engineered memory shortage? $MU just crashed over 17% from its recent high right as Micron, Samsung, and SK Hynix got hit with a federal lawsuit accusing them of restricting DRAM supply while AI demand sent prices up nearly 700%. The scary part? Samsung and SK Hynix already pleaded guilty to memory chip price fixing once before. And now the entire AI boom runs on their chips.
0:21

What if the AI bubble was built on an engineered memory shortage? $MU just crashed over 17% from its recent high right as Micron, Samsung, and SK Hynix got hit with a federal lawsuit accusing them of restricting DRAM supply while AI demand sent prices up nearly 700%. The scary part? Samsung and SK Hynix already pleaded guilty to memory chip price fixing once before. And now the entire AI boom runs on their chips.

Crypto Rover

96,810 次观看 • 2 个月前

$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."
1:53

$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."

Trade Whisperer

180,403 次观看 • 1 个月前

Teaming up with SanDisk to build the next standard for AI inference. As a full-stack AI memory creator, SK hynix is launching the global standardization of #HBF with Sandisk & Open Compute Project (OCP). HBF is the new memory layer bridging the gap between #HBM and #SSD. #SKhynix
1:25

Teaming up with SanDisk to build the next standard for AI inference. As a full-stack AI memory creator, SK hynix is launching the global standardization of #HBF with Sandisk & Open Compute Project (OCP). HBF is the new memory layer bridging the gap between #HBM and #SSD. #SKhynix

SK hynix

194,727 次观看 • 6 个月前

Breaking the Memory Wall: Micron’s Strategy for the AI Era Ben Bajarin and Jay Goldberg 顾忠南 talk micron's strategy for the AI era with Jeremy Werner SVP and GM of Micron’s Core Data Center Business Unit. Chapters: (00:00) Introduction and Guest Welcome (01:12) How the AI Cycle Differs from Past Volatility (04:23) Anticipating the Explosive Growth of AI (06:40) The Long-Term Sustainability of the AI Market (10:07) The "Memory Wall" in AI Inference (12:05) Explaining KV Cache and Context Windows (15:07) The Memory Hierarchy: From HBM to SSDs (20:21) Scaling for Billions of Simultaneous Users (23:05) Solving the Power and Efficiency Bottleneck (26:27) Why AI is Driving a Storage Shortage (29:57) Personal AI Agents and Persistent Memory Needs (34:13) Innovations in Ultra-High-Capacity SSDs (39:00) Accelerating the Pace of Engineering Innovation (43:50) Market Misunderstandings and the Future Potential of AI (47:38) The Global Race to Build New Fabs (51:34) Closing Remarks
52:31

Breaking the Memory Wall: Micron’s Strategy for the AI Era Ben Bajarin and Jay Goldberg 顾忠南 talk micron's strategy for the AI era with Jeremy Werner SVP and GM of Micron’s Core Data Center Business Unit. Chapters: (00:00) Introduction and Guest Welcome (01:12) How the AI Cycle Differs from Past Volatility (04:23) Anticipating the Explosive Growth of AI (06:40) The Long-Term Sustainability of the AI Market (10:07) The "Memory Wall" in AI Inference (12:05) Explaining KV Cache and Context Windows (15:07) The Memory Hierarchy: From HBM to SSDs (20:21) Scaling for Billions of Simultaneous Users (23:05) Solving the Power and Efficiency Bottleneck (26:27) Why AI is Driving a Storage Shortage (29:57) Personal AI Agents and Persistent Memory Needs (34:13) Innovations in Ultra-High-Capacity SSDs (39:00) Accelerating the Pace of Engineering Innovation (43:50) Market Misunderstandings and the Future Potential of AI (47:38) The Global Race to Build New Fabs (51:34) Closing Remarks

The Circuit

94,438 次观看 • 3 个月前

Elon Musk just described a project so large that most people will assume he is exaggerating (Save this). He is not. In the video, Musk lays out the central problem facing every AI company on earth, the entire global chip industry is on a path to produce roughly 100 gigawatts of AI compute per year. That sounds like a lot until you understand that his companies alone Tesla, SpaceX, and xAI will need orders of magnitude more than that. His answer is the TerraFab. It is a joint chip factory spanning 100 million square feet, ten times the size of Tesla's Gigafactory Texas announced in March 2026, with Grimes County, Texas commissioners approving the full scale facility site just last week. The goal is one full terawatt of AI compute output per year. For context, 1 terawatt is 1,000 gigawatts twice the current total electricity consumption of the United States. SpaceX has already committed an initial $55 billion to the prototype phase, with total investment estimates ranging into the trillions. Here is why this matters for Micron specifically. In the video, Musk named Nvidia's Rubin chips as the reference design for TerraFab's first orbital deployments, and said "You're going to need a lot of memory to go with that." A billion full radical equivalent chips per year, each requiring stacks of high bandwidth memory, that is the demand signal Micron just received from one of the most capital-intensive projects in human history. And Micron already cannot keep up with what exists today. Micron's entire 2026 HBM output is fully sold out contracted before the year began. HBM4 entered volume production ahead of schedule and sold out immediately. The structural reason Micron wins here is simple. Every AI chip ever built Nvidia H100s, Rubin chips, custom ASICs, TPUs is useless without high-bandwidth memory stacked directly on top of it. There are only three companies in the world that supply HBM at scale, Samsung, SK Hynix, and Micron. Samsung has had quality issues, SK Hynix is supply constrained. Micron is the only US headquartered HBM manufacturer which matters enormously given CHIPS Act subsidies, domestic procurement requirements, and the political push to keep critical AI memory production on American soil. TerraFab just made the memory deficit permanently larger. Come join Milk Road Pro for our full breakdown of Micron and our entire AI thesis just for $1. Link below!
3:30

Elon Musk just described a project so large that most people will assume he is exaggerating (Save this). He is not. In the video, Musk lays out the central problem facing every AI company on earth, the entire global chip industry is on a path to produce roughly 100 gigawatts of AI compute per year. That sounds like a lot until you understand that his companies alone Tesla, SpaceX, and xAI will need orders of magnitude more than that. His answer is the TerraFab. It is a joint chip factory spanning 100 million square feet, ten times the size of Tesla's Gigafactory Texas announced in March 2026, with Grimes County, Texas commissioners approving the full scale facility site just last week. The goal is one full terawatt of AI compute output per year. For context, 1 terawatt is 1,000 gigawatts twice the current total electricity consumption of the United States. SpaceX has already committed an initial $55 billion to the prototype phase, with total investment estimates ranging into the trillions. Here is why this matters for Micron specifically. In the video, Musk named Nvidia's Rubin chips as the reference design for TerraFab's first orbital deployments, and said "You're going to need a lot of memory to go with that." A billion full radical equivalent chips per year, each requiring stacks of high bandwidth memory, that is the demand signal Micron just received from one of the most capital-intensive projects in human history. And Micron already cannot keep up with what exists today. Micron's entire 2026 HBM output is fully sold out contracted before the year began. HBM4 entered volume production ahead of schedule and sold out immediately. The structural reason Micron wins here is simple. Every AI chip ever built Nvidia H100s, Rubin chips, custom ASICs, TPUs is useless without high-bandwidth memory stacked directly on top of it. There are only three companies in the world that supply HBM at scale, Samsung, SK Hynix, and Micron. Samsung has had quality issues, SK Hynix is supply constrained. Micron is the only US headquartered HBM manufacturer which matters enormously given CHIPS Act subsidies, domestic procurement requirements, and the political push to keep critical AI memory production on American soil. TerraFab just made the memory deficit permanently larger. Come join Milk Road Pro for our full breakdown of Micron and our entire AI thesis just for $1. Link below!

Milk Road AI

248,089 次观看 • 2 个月前

$MU CEO Sanjay Mehrotra said Micron can only supply ~60% of what key customers are requesting as AI demand creates an unprecedented memory shortage. Every wafer shifted into HBM can cost ~3x the standard DRAM output and demand keeps rising because inference, agents, robotics and autonomous systems are all memory-bandwidth-bound. Memory fab capacity takes three to five years to build so the shortage Mehrotra is describing today could define the supply curve for the rest of the decade.
0:28

$MU CEO Sanjay Mehrotra said Micron can only supply ~60% of what key customers are requesting as AI demand creates an unprecedented memory shortage. Every wafer shifted into HBM can cost ~3x the standard DRAM output and demand keeps rising because inference, agents, robotics and autonomous systems are all memory-bandwidth-bound. Memory fab capacity takes three to five years to build so the shortage Mehrotra is describing today could define the supply curve for the rest of the decade.

Shay Boloor

125,563 次观看 • 3 个月前

DAN NILES SAYS MONEY IS COMING OUT OF MICRON -SK Hynix trades 20-30% cheaper than $MU and got high bandwidth memory running first -Expects money to rotate out of $MU and $SNDK now that US investors finally have a third way to play memory -Says the AI buildout money looks infinite right now, but "money is not infinite" Dan Niles
4:44

DAN NILES SAYS MONEY IS COMING OUT OF MICRON -SK Hynix trades 20-30% cheaper than $MU and got high bandwidth memory running first -Expects money to rotate out of $MU and $SNDK now that US investors finally have a third way to play memory -Says the AI buildout money looks infinite right now, but "money is not infinite" Dan Niles

WOLF

88,484 次观看 • 1 个月前

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.
0:50

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.

Fireside Alpha

38,370 次观看 • 2 个月前