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"If the multiples on the power, cooling, optical names are correct... Nvidia, memory, they're going up a lot" Gavin Baker on The All-In Podcast Since then: Micron near 6x forward, NVIDIA 23x, Vertiv 52x, and memory just sold off again Watch Same rack, different multiples

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31,037 Aufrufe • vor 18 Tagen •via X (Twitter)

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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."

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 Aufrufe • vor 1 Monat

$MU : 12 months before the move was obvious. $MRVL : 2 months in. Jordi Visser made 4-8x on memory and exited. Marvell (optical networking) is now where Micron was early in its arc - two months into the breakout vs. the year of runway Micron had. Same setup. Earlier in the cycle. Full podcast brief in 7 minute read -

$MU : 12 months before the move was obvious. $MRVL : 2 months in. Jordi Visser made 4-8x on memory and exited. Marvell (optical networking) is now where Micron was early in its arc - two months into the breakout vs. the year of runway Micron had. Same setup. Earlier in the cycle. Full podcast brief in 7 minute read -

Podcast Alpha

70,182 Aufrufe • vor 2 Monaten

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

128,678 Aufrufe • vor 1 Monat

◈@Exla_ai speeds up AI on edge devices (e.g. NVIDIA Jetsons) with advanced quantization. Reduce memory use by up to 80% and boost inference speed by 3x–20x— all with just a few lines of code. Congrats on the launch, Viraat & Pranav!

◈@Exla_ai speeds up AI on edge devices (e.g. NVIDIA Jetsons) with advanced quantization. Reduce memory use by up to 80% and boost inference speed by 3x–20x— all with just a few lines of code. Congrats on the launch, Viraat & Pranav!

Y Combinator

19,937 Aufrufe • vor 1 Jahr

What's the next big trade in AI infrastructure after memory? We asked Meltem Demirors and Kelly Greer: "Memory is ripping now. We think the next narrative is gonna be around networking." "Jensen is doing the exact same thing now that he did with Mellanox in 2019 that turned NVIDIA from a GPU company into a systems and networking company. He's doing it again with Marvell." "Hyperscalers are literally telling you the two biggest bottlenecks for AI spend right now are networking and memory." "The tea leaves are written literally everywhere... the market's just been on the memory track. We're trying to get ahead of that."

What's the next big trade in AI infrastructure after memory? We asked Meltem Demirors and Kelly Greer: "Memory is ripping now. We think the next narrative is gonna be around networking." "Jensen is doing the exact same thing now that he did with Mellanox in 2019 that turned NVIDIA from a GPU company into a systems and networking company. He's doing it again with Marvell." "Hyperscalers are literally telling you the two biggest bottlenecks for AI spend right now are networking and memory." "The tea leaves are written literally everywhere... the market's just been on the memory track. We're trying to get ahead of that."

MTS

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Jensen just unveiled the new NVIDIA Jetson Orin Nano Super, optimized for on-device AI processing in robots, boasting 70 trillion operations (INT8) per second, 102GB/s memory bandwidth, 25 watts power consumption, and a price of $249.

Jensen just unveiled the new NVIDIA Jetson Orin Nano Super, optimized for on-device AI processing in robots, boasting 70 trillion operations (INT8) per second, 102GB/s memory bandwidth, 25 watts power consumption, and a price of $249.

The Humanoid Hub

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I told you to keep buying memory stocks at my levels. Memory has a chance for a low forever, however; we are NOT out of the woods just yet. Here's exactly what I expect moving forward: 1. Memory ETF $DRAM 2. SanDisk $SNDK 3. Micron $MU Literally all my trades, buy and sell signals are in Discord @

I told you to keep buying memory stocks at my levels. Memory has a chance for a low forever, however; we are NOT out of the woods just yet. Here's exactly what I expect moving forward: 1. Memory ETF $DRAM 2. SanDisk $SNDK 3. Micron $MU Literally all my trades, buy and sell signals are in Discord @

StockWhale

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Gavin Baker on the one variable that now dominates all others in AI compute: "the more memory you put with flop for a given unit of compute, the more tokens you get out -- it's the single most important thing you could do to increase token output per unit of compute." This is him laying out why the whole AI buildout now turns on memory, why breaking a supply agreement can end your entire franchise, and what Nvidia quietly writes into every equity check it signs. "You might blow up your entire business and your franchise by breaking an LTA." "If you break your LTA and then in the next two or three years for any reason leverage shifts back to the memory guys -- you're out of business." "Essentially every time they haven't taken an equity stake in something, it's been a mistake, they've taken equity stakes in everything essentially except the memory companies." Bookmark & watch ↓

Gavin Baker on the one variable that now dominates all others in AI compute: "the more memory you put with flop for a given unit of compute, the more tokens you get out -- it's the single most important thing you could do to increase token output per unit of compute." This is him laying out why the whole AI buildout now turns on memory, why breaking a supply agreement can end your entire franchise, and what Nvidia quietly writes into every equity check it signs. "You might blow up your entire business and your franchise by breaking an LTA." "If you break your LTA and then in the next two or three years for any reason leverage shifts back to the memory guys -- you're out of business." "Essentially every time they haven't taken an equity stake in something, it's been a mistake, they've taken equity stakes in everything essentially except the memory companies." Bookmark & watch ↓

Fireside Alpha

18,521 Aufrufe • vor 11 Tagen

Gavin Baker: "if you're a foundation model company and you do not have unique data and internet scale distribution, you are the fastest depreciating asset in human history - I think most of these companies are zeros, and there's like 10 of them" this is him explaining why the 100 largest buildings in the world will be data centers, why there are only three places left in America you can legally build one, and what he says is setting up the first real memory cycle since 1996 "in our lifetime there will be data centers that are bigger than Manhattan" "the two rate limiting factors for AI are no longer compute and GPUs - it's power, finding places to plug the GPUs in" "there's only three places in the United States you can put one of these - it's got to be near multiple nuclear reactors that are unregulated within 50 miles of each other" "if Apple doubles the memory in the iPhone and Google does the same for Android at the same time, you could have the first real memory cycle since 1996 - and in 1996 Micron was a 50 bagger" "the only hope most of these companies have is for Amazon to acquire them the way Microsoft did with Inflection" bookmark & watch the full conversation ↓

Gavin Baker: "if you're a foundation model company and you do not have unique data and internet scale distribution, you are the fastest depreciating asset in human history - I think most of these companies are zeros, and there's like 10 of them" this is him explaining why the 100 largest buildings in the world will be data centers, why there are only three places left in America you can legally build one, and what he says is setting up the first real memory cycle since 1996 "in our lifetime there will be data centers that are bigger than Manhattan" "the two rate limiting factors for AI are no longer compute and GPUs - it's power, finding places to plug the GPUs in" "there's only three places in the United States you can put one of these - it's got to be near multiple nuclear reactors that are unregulated within 50 miles of each other" "if Apple doubles the memory in the iPhone and Google does the same for Android at the same time, you could have the first real memory cycle since 1996 - and in 1996 Micron was a 50 bagger" "the only hope most of these companies have is for Amazon to acquire them the way Microsoft did with Inflection" bookmark & watch the full conversation ↓

Сarm1ne

274,201 Aufrufe • vor 13 Tagen

NVIDIA’s new humanoid robot brain is officially on sale for $3,499. NVIDIA has announced the general availability of the Jetson AGX Thor developer kit and production modules. Powered by an NVIDIA Blackwell GPU and featuring 128GB of memory, it delivers up to 2,070 FP4 teraflops within a 130-watt power envelope. Early adopters include Agility, Boston Dynamics, and Figure.

NVIDIA’s new humanoid robot brain is officially on sale for $3,499. NVIDIA has announced the general availability of the Jetson AGX Thor developer kit and production modules. Powered by an NVIDIA Blackwell GPU and featuring 128GB of memory, it delivers up to 2,070 FP4 teraflops within a 130-watt power envelope. Early adopters include Agility, Boston Dynamics, and Figure.

The Humanoid Hub

369,047 Aufrufe • vor 11 Monaten

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.

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 Aufrufe • vor 4 Monaten

Gavin Baker says the Anthropic S-1 will break a lot of investors' brains "The Anthropic S-1 is going to be really important. And it's going to break a lot of people's brains. There are a lot of macro and value investors who are very confidently making these prognostications about AI with the assumption that tokens are subsidized." "Anthropic is generating cash. They're profitable. Open source tokens are profitable. OpenAI, if they're not generating cash, they will be imminently. SpaceX, same thing." "These macro and value investors are ignorant. They're very smart, but they're ignorant of the facts. They're making an assumption that tokens are subsidized and this is all going to collapse in some circular financing bonfire." "They're just wrong. The overwhelming majority of tokens are profitable for everyone in the chain. Everyone." "It's saying I'm very bearish on the world economy because oil is at $500 a barrel. If oil was at $500 a barrel, that'd be a good reason to be bearish. It's just not. They're just wrong. The central fact is wrong."

Gavin Baker says the Anthropic S-1 will break a lot of investors' brains "The Anthropic S-1 is going to be really important. And it's going to break a lot of people's brains. There are a lot of macro and value investors who are very confidently making these prognostications about AI with the assumption that tokens are subsidized." "Anthropic is generating cash. They're profitable. Open source tokens are profitable. OpenAI, if they're not generating cash, they will be imminently. SpaceX, same thing." "These macro and value investors are ignorant. They're very smart, but they're ignorant of the facts. They're making an assumption that tokens are subsidized and this is all going to collapse in some circular financing bonfire." "They're just wrong. The overwhelming majority of tokens are profitable for everyone in the chain. Everyone." "It's saying I'm very bearish on the world economy because oil is at $500 a barrel. If oil was at $500 a barrel, that'd be a good reason to be bearish. It's just not. They're just wrong. The central fact is wrong."

dnap

163,768 Aufrufe • vor 21 Stunden

The NVIDIA Vera Rubin NVL72 scale-up fabric, built on the sixth-generation NVIDIA NVLink. The NVLink 6 Switch trays connect 72 Rubin GPUs in an all-to-all configuration. The NVLink spine—over 5,000 copper cables make up the vertical NVLink traverse. It securely moves more data than the entire bandwidth of the internet. The single-wide NVIDIA MGX rack design unlocks a cable-free, horizontal NVLink scale-up traverse, running directly on PCB for resiliency at scale. This is how NVIDIA Vera Rubin NVL72 works as one, designed to deliver the lowest token cost and 10x more performance per watt than the previous generation. #NVIDIAVeraRubin

The NVIDIA Vera Rubin NVL72 scale-up fabric, built on the sixth-generation NVIDIA NVLink. The NVLink 6 Switch trays connect 72 Rubin GPUs in an all-to-all configuration. The NVLink spine—over 5,000 copper cables make up the vertical NVLink traverse. It securely moves more data than the entire bandwidth of the internet. The single-wide NVIDIA MGX rack design unlocks a cable-free, horizontal NVLink scale-up traverse, running directly on PCB for resiliency at scale. This is how NVIDIA Vera Rubin NVL72 works as one, designed to deliver the lowest token cost and 10x more performance per watt than the previous generation. #NVIDIAVeraRubin

NVIDIA AI Infrastructure

38,456 Aufrufe • vor 1 Monat

Bank of America just upgraded $MU and memory saying that AI hardware infrastructure spending "is not slowing down." They also said that "the market is underestimating" Micron stock and note it as a "time to buy." Buy every major dip on memory and you will be rich in the future. 1. Sandisk $SNDK 2. Micron $MU All my buy and sell signals in Discord @

Bank of America just upgraded $MU and memory saying that AI hardware infrastructure spending "is not slowing down." They also said that "the market is underestimating" Micron stock and note it as a "time to buy." Buy every major dip on memory and you will be rich in the future. 1. Sandisk $SNDK 2. Micron $MU All my buy and sell signals in Discord @

StockWhale

69,013 Aufrufe • vor 1 Monat

Gavin Baker accurately predicted Micron's 10x run in 2025 Now he's making his next big call: "DRAM is probably going to be 30 to 40% of all hyperscaler CapEx next year, every hundreds of billions of dollars spent goes straight to DRAM" He calls it the single most important bottleneck in all of AI, above lasers, power chips, NAND or HDDs, Elon is even turning the TeraFab toward memory because he sees the same thing Only three firms on earth can make the memory these AI servers need "This is as close to magic as science can get" And Micron just locked in supply contracts covering half its revenue, with floor prices already above the margin peaks of past cycles, the economics of the stock are not what they were "If you still hold $MU at a commodity discount to $ASML and $LRCX, that discount is no longer earned" bookmark it, one of the strongest memory theses for 2026-2027 ↓

Gavin Baker accurately predicted Micron's 10x run in 2025 Now he's making his next big call: "DRAM is probably going to be 30 to 40% of all hyperscaler CapEx next year, every hundreds of billions of dollars spent goes straight to DRAM" He calls it the single most important bottleneck in all of AI, above lasers, power chips, NAND or HDDs, Elon is even turning the TeraFab toward memory because he sees the same thing Only three firms on earth can make the memory these AI servers need "This is as close to magic as science can get" And Micron just locked in supply contracts covering half its revenue, with floor prices already above the margin peaks of past cycles, the economics of the stock are not what they were "If you still hold $MU at a commodity discount to $ASML and $LRCX, that discount is no longer earned" bookmark it, one of the strongest memory theses for 2026-2027 ↓

Damir Akaza

820,729 Aufrufe • vor 1 Monat

Surface Laptop Ultra is the closest Microsoft has ever come to building a no‑compromise performance machine, and the details are interesting to say the least. A full NVIDIA RTX Spark platform, up to 128GB of unified memory, a 15‑inch mini‑LED display hitting 2,000 nits, native Arm gaming progress, and a cooling system built for sustained heavy workloads. What Microsoft and NVIDIA are doing together here raises real questions about where Windows on Arm goes next and how this shifts expectations for pro‑grade laptops. #Surface (1/2)

Surface Laptop Ultra is the closest Microsoft has ever come to building a no‑compromise performance machine, and the details are interesting to say the least. A full NVIDIA RTX Spark platform, up to 128GB of unified memory, a 15‑inch mini‑LED display hitting 2,000 nits, native Arm gaming progress, and a cooling system built for sustained heavy workloads. What Microsoft and NVIDIA are doing together here raises real questions about where Windows on Arm goes next and how this shifts expectations for pro‑grade laptops. #Surface (1/2)

Windows Central

13,939 Aufrufe • vor 2 Monaten

🚨 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?

🚨 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 Aufrufe • vor 1 Monat

Terafab is starting to sound like a lot more than just a chip factory! Tesla and SpaceX are reportedly putting $16.8B into phase one, with plans to handle everything from chip design and fabrication to memory, packaging and testing. The idea is basically to bring pieces of what TSMC, Micron and NVIDIA do under one roof. And the chips could eventually power everything from Tesla cars and Optimus to SpaceX data centers and AI compute in space. $TSLA $SPCX

Terafab is starting to sound like a lot more than just a chip factory! Tesla and SpaceX are reportedly putting $16.8B into phase one, with plans to handle everything from chip design and fabrication to memory, packaging and testing. The idea is basically to bring pieces of what TSMC, Micron and NVIDIA do under one roof. And the chips could eventually power everything from Tesla cars and Optimus to SpaceX data centers and AI compute in space. $TSLA $SPCX

Herbert Ong

29,062 Aufrufe • vor 6 Tagen