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Jensen - "We know that every single GPU that Nvidia has in the cloud, across aws and gcp, is rented out. They’re all fully utilized. Demand for gpus is so high that even the spot prices of gpus sold a couple of years ago are going up."

The AI Investor

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69,299 次观看 • 6 个月前 •via X (Twitter)

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$NVDA CEO Jensen Huang says GPUs are effectively sold out across the cloud with availability so tight that even renting older-generation chips has become difficult. Spot prices for GPU rentals are rising as a result which favors $NBIS, $IREN & $CIFR as demand pushes through limited capacity.

$NVDA CEO Jensen Huang says GPUs are effectively sold out across the cloud with availability so tight that even renting older-generation chips has become difficult. Spot prices for GPU rentals are rising as a result which favors $NBIS, $IREN & $CIFR as demand pushes through limited capacity.

Shay Boloor

212,563 次观看 • 6 个月前

"GPUs are effectively sold out across the cloud with availability so tight that even renting older-generation chips has become difficult," Nvidia, $NVDA, CEO has said.

"GPUs are effectively sold out across the cloud with availability so tight that even renting older-generation chips has become difficult," Nvidia, $NVDA, CEO has said.

unusual_whales

241,294 次观看 • 6 个月前

Jensen Huang: “Demand is so strong that rental prices of 2 generation old GPUs are going up.” Jensen said this in January and Blackwell rental prices are up by 50% since then. The demand is so excessive now that Anthropic had to reduce default thinking effort of Claude to be able to serve everybody. There is simply not enough compute. $AMD $NVDA $NBIS $AMZN $ORCL

Jensen Huang: “Demand is so strong that rental prices of 2 generation old GPUs are going up.” Jensen said this in January and Blackwell rental prices are up by 50% since then. The demand is so excessive now that Anthropic had to reduce default thinking effort of Claude to be able to serve everybody. There is simply not enough compute. $AMD $NVDA $NBIS $AMZN $ORCL

Oguz Erkan

73,615 次观看 • 3 个月前

“The Coatue team mapped out cloud revenue market share, Oracle at 5%, Amazon at 44% with AWS, Google 19%, Microsoft 30%. Next to that, they showed NVIDIA GPU allocation: Microsoft and Google match their cloud share. Amazon is 44% cloud revenue but only 20% GPUs. Oracle jumps from 5% to 19%. Cori comes out of nowhere at 11%.” “One obvious takeaway is that Amazon has half the share of GPUs than their share of AWS. So that could mean one of two things, either AWS is behind in AI, that could be one, or they're pursuing a different hardware strategy than its competitors.” — Bill Gurley Bill Gurley and Thomas Laffont Thomas Laffont

“The Coatue team mapped out cloud revenue market share, Oracle at 5%, Amazon at 44% with AWS, Google 19%, Microsoft 30%. Next to that, they showed NVIDIA GPU allocation: Microsoft and Google match their cloud share. Amazon is 44% cloud revenue but only 20% GPUs. Oracle jumps from 5% to 19%. Cori comes out of nowhere at 11%.” “One obvious takeaway is that Amazon has half the share of GPUs than their share of AWS. So that could mean one of two things, either AWS is behind in AI, that could be one, or they're pursuing a different hardware strategy than its competitors.” — Bill Gurley Bill Gurley and Thomas Laffont Thomas Laffont

bg2clips

180,310 次观看 • 1 年前

During the dotcom bubble, 97% of America’s fiber was dark because infrastructure was built years ahead of demand. Today’s AI buildout looks nothing like that. The comment “there are no dark GPUs” means every GPU produced is already reserved or monetized the moment it ships. This is the difference between speculative infrastructure & fully utilized infrastructure.

During the dotcom bubble, 97% of America’s fiber was dark because infrastructure was built years ahead of demand. Today’s AI buildout looks nothing like that. The comment “there are no dark GPUs” means every GPU produced is already reserved or monetized the moment it ships. This is the difference between speculative infrastructure & fully utilized infrastructure.

Shay Boloor

545,174 次观看 • 9 个月前

Gavin Baker (Gavin Baker) says the disaggregation of inference can extend GPU useful lives from 3-4 years to 10-15. That may single-handedly save private credit and reduce the financing rates for GPUs, which will drive demand and help finance the build-out. "The disaggregation of prefill and inference is going to be amazing for the useful lives of GPU and may single-handedly save private credit. Private credit is in pain from these SaaS loans. But there's a lot of private credit in GPUs too. They were underwriting that to 3-4. The disaggregation of inference means that these GPUs are going to have 10 or 15-year lives. The AI skeptics are like, "Oh, these companies are all cooking their books. The useful life of a GPU is only a year or two. The useful life of a CPU is only four years because the rapid technological change." No. What rapid technological change has done with the disaggregation of prefill and inference is you can put a Cerebras system or Groq LPUs effectively in front of a Hopper or even an Ampere, use that Hopper and Ampere for prefill, and extend the useful life of that GPU until it melts. This is going to be really good for the whole private credit industry. It's gonna help finance the AI build-out. Because if you can start to finance GPUs at 5% or 6% instead of – I think CoreWeave's lowest financing was low sevens – that actually mathematically changes the cost to finance this build-out."

Gavin Baker (Gavin Baker) says the disaggregation of inference can extend GPU useful lives from 3-4 years to 10-15. That may single-handedly save private credit and reduce the financing rates for GPUs, which will drive demand and help finance the build-out. "The disaggregation of prefill and inference is going to be amazing for the useful lives of GPU and may single-handedly save private credit. Private credit is in pain from these SaaS loans. But there's a lot of private credit in GPUs too. They were underwriting that to 3-4. The disaggregation of inference means that these GPUs are going to have 10 or 15-year lives. The AI skeptics are like, "Oh, these companies are all cooking their books. The useful life of a GPU is only a year or two. The useful life of a CPU is only four years because the rapid technological change." No. What rapid technological change has done with the disaggregation of prefill and inference is you can put a Cerebras system or Groq LPUs effectively in front of a Hopper or even an Ampere, use that Hopper and Ampere for prefill, and extend the useful life of that GPU until it melts. This is going to be really good for the whole private credit industry. It's gonna help finance the AI build-out. Because if you can start to finance GPUs at 5% or 6% instead of – I think CoreWeave's lowest financing was low sevens – that actually mathematically changes the cost to finance this build-out."

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CoreWeave cofounder brannin mcbee says older GPUs and servers still have strong demand, even as new generations ramp up: "Our pricing on Ampere went up during 2025, and our pricing on Hopper stayed within 10% or so of where we started the year." "Why is that happening? I think it's largely driven by inference and the idea that—not every workload needs the latest, greatest GPUs or servers." "There are different types of workloads that need different compute, and the market is efficiently pairing the right GPUs and servers with the workloads that need them." "We see incredibly robust demand all the way back to the Ampere generation, which I believe is six years old at this point."

CoreWeave cofounder brannin mcbee says older GPUs and servers still have strong demand, even as new generations ramp up: "Our pricing on Ampere went up during 2025, and our pricing on Hopper stayed within 10% or so of where we started the year." "Why is that happening? I think it's largely driven by inference and the idea that—not every workload needs the latest, greatest GPUs or servers." "There are different types of workloads that need different compute, and the market is efficiently pairing the right GPUs and servers with the workloads that need them." "We see incredibly robust demand all the way back to the Ampere generation, which I believe is six years old at this point."

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JD Vance: Gas prices are up and we know they're up and we know that people are hurting because of it. This is a temporary blip. What happened under the Biden administration is that gas prices were high for 4 years…

JD Vance: Gas prices are up and we know they're up and we know that people are hurting because of it. This is a temporary blip. What happened under the Biden administration is that gas prices were high for 4 years…

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Holy smokes, BULLISH $AMZN.. “Even with all of this investment – my best estimate is that we will be capacity constrained for the next couple of years. We will sell EVERY SINGLE server, and every single bit – and we will wish that we had more…” – Matt Garman, AWS CEO $AMZN

Holy smokes, BULLISH $AMZN.. “Even with all of this investment – my best estimate is that we will be capacity constrained for the next couple of years. We will sell EVERY SINGLE server, and every single bit – and we will wish that we had more…” – Matt Garman, AWS CEO $AMZN

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David Sacks on breaking up Nvidia and chip competition: "Jensen has been working on this for 30 years. When everyone else was focused on CPUs, he had this vision around GPUs. And so, you know, in America we don't punish winning, we don't punish success that comes as a result of being visionary." $NVDA

David Sacks on breaking up Nvidia and chip competition: "Jensen has been working on this for 30 years. When everyone else was focused on CPUs, he had this vision around GPUs. And so, you know, in America we don't punish winning, we don't punish success that comes as a result of being visionary." $NVDA

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🚨NVIDIA UNVEILS 72-GPU SUPERCHIP SET TO REVOLUTIONIZE AI Jensen Huang introduced the GB200 NVL72, a data center powerhouse packing 72 Blackwell GPUs, 1.4 exaFLOPS of compute, and 130 trillion transistors. This "superchip" enables test-time scaling, a groundbreaking approach that lets AI reason in real time, mimicking human thought. Huang highlighted AI agents as a multi-trillion dollar opportunity, with NVIDIA leading the charge. From NeMo for deploying AI agents to cutting-edge GPUs, NVIDIA is cementing itself as the backbone of the AI economy across industries. Source: CES 2025, NVIDIA Tsarathustra

🚨NVIDIA UNVEILS 72-GPU SUPERCHIP SET TO REVOLUTIONIZE AI Jensen Huang introduced the GB200 NVL72, a data center powerhouse packing 72 Blackwell GPUs, 1.4 exaFLOPS of compute, and 130 trillion transistors. This "superchip" enables test-time scaling, a groundbreaking approach that lets AI reason in real time, mimicking human thought. Huang highlighted AI agents as a multi-trillion dollar opportunity, with NVIDIA leading the charge. From NeMo for deploying AI agents to cutting-edge GPUs, NVIDIA is cementing itself as the backbone of the AI economy across industries. Source: CES 2025, NVIDIA Tsarathustra

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"Compute is the currency of the future." "Compute is the new OIL." Great, so how do I invest? You can play with the NVIDIA stock, or spin up an AI-Ready Data Center. Challenges of Spinning up AI-Ready Compute: Cost: Even a small cluster of H100 machines is upward to $10M GPU Scarcity: Unallocated or “underutilized” H100, A100, and RTX4090 GPUs are almost non-existent. Very Specific Physical & Technical Requirements: A data center with the right infrastructure is also very rare. There are only 10 in North America that deploy thousands of GPUs, in the open market. Domain Expertise: Teams of experts with the years of experience required to operate, deploy, and manage AI compute at scale are as rare as the GPUs they deploy. Exabits checks all of these boxes and we are the premier Compute Tokenization Platform. We are backed by REAL COMPUTE used by REAL CUSTOMERS.

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Jensen Huang: “We need memory for… HBM memory for performance. We need LPDDR for low-power memory.” Reporter: Is memory constraint an issue for NVIDIA this year? Jensen Huang: “The entire supply chain is challenging this year because demand is so much more. Supply has been growing 100% every year, but demand is going faster than that. We are going to have a very good year.” We are entering a Memory Supercycle $MU

Jensen Huang: “We need memory for… HBM memory for performance. We need LPDDR for low-power memory.” Reporter: Is memory constraint an issue for NVIDIA this year? Jensen Huang: “The entire supply chain is challenging this year because demand is so much more. Supply has been growing 100% every year, but demand is going faster than that. We are going to have a very good year.” We are entering a Memory Supercycle $MU

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Baseten CEO Tuhin Srivastava tells Altimeter's Apoorv Agrawal that one of Baseten's cloud providers has already indicated their B200 prices ($/GPU hour) are set to double when existing contracts expire and are up for renewal later this year. "If you go out right now saying you want a thousand GPUs, truly.. people are talking about Q2 of next year. So 12 months out, maybe 15 months out. We have a cluster.. in one of these clouds.. of B200s.. Our unit price right now is $2.63 an hour.. that's up for renewal in October. They came to us already in May and said $5.10 is the new price.. So double."

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Per HIVE Digital Technologies CEO Aydin Kilic, some GPUs are retaining as much as 80% of their value years after being deployed 🤯 The reason? AI demand is insatiable. "Obviously China has a huge amount of demand and they're trying to get whatever GPUs they can. Maybe they can't buy the latest and greatest, but they're allowed to buy the older gear. So they'll take all that stuff."

Per HIVE Digital Technologies CEO Aydin Kilic, some GPUs are retaining as much as 80% of their value years after being deployed 🤯 The reason? AI demand is insatiable. "Obviously China has a huge amount of demand and they're trying to get whatever GPUs they can. Maybe they can't buy the latest and greatest, but they're allowed to buy the older gear. So they'll take all that stuff."

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In 2009, Charlie Rose asked Jensen Huang about Nvidia vs. Intel. The chipmakers weren’t directly competing but Jensen said GPU vs. CPU was a “battle for the soul” of computing and GPUs would be “more relevant” in time. Intel was worth $100B and Nvidia was at $4B. Today, Intel is at $565B while Nvidia is worth over 1,200x more at $5 trillion.

In 2009, Charlie Rose asked Jensen Huang about Nvidia vs. Intel. The chipmakers weren’t directly competing but Jensen said GPU vs. CPU was a “battle for the soul” of computing and GPUs would be “more relevant” in time. Intel was worth $100B and Nvidia was at $4B. Today, Intel is at $565B while Nvidia is worth over 1,200x more at $5 trillion.

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In 2009, Charlie Rose asked Jensen Huang about Nvidia vs. Intel. The chipmakers weren’t directly competing but Jensen said GPU vs. CPU was a “battle for the soul” of computing and GPUs would be “more relevant” in time. Intel was worth $100B and Nvidia was at $4B. Today, Intel is at $107B while Nvidia is worth ~1,000x more at $4.4 Trillion.

In 2009, Charlie Rose asked Jensen Huang about Nvidia vs. Intel. The chipmakers weren’t directly competing but Jensen said GPU vs. CPU was a “battle for the soul” of computing and GPUs would be “more relevant” in time. Intel was worth $100B and Nvidia was at $4B. Today, Intel is at $107B while Nvidia is worth ~1,000x more at $4.4 Trillion.

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320,052 次观看 • 11 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.

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.

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Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.

Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.

Brian Roemmele

87,188 次观看 • 2 年前

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Over 2,000 NVIDIA B300 GPUs are going live in a mega-cluster on Aethir's decentralized cloud very soon. ☁️ This is next-level infrastructure that unlocks access for the most ambitious AI workloads and use cases. 💻 Enterprise-grade GPU compute, ready to scale with your ambitions. ⚡️ Listen to Mark Rydon, our Co-Founder, explain the importance of Aethir’s B300 deployment.

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