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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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$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.

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"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.

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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

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“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

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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.

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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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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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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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285,325 просмотров • 21 дней назад

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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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

87,116 просмотров • 2 лет назад