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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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.Peptone is a biotech company that uses NVIDIA GPUs on AWS infrastructure for AI-driven drug discovery. They are working towards the goal of developing drugs against not only tumors but also inflammatory diseases and neurodegenerative diseases. AWS cloud operations. The ability to scale from zero to hundreds of GPUs in a matter of minutes. NVIDIA's reliability, availability, and cost of GPUs. All aiding Peptone in AI-driven drug discovery.

.Peptone is a biotech company that uses NVIDIA GPUs on AWS infrastructure for AI-driven drug discovery. They are working towards the goal of developing drugs against not only tumors but also inflammatory diseases and neurodegenerative diseases. AWS cloud operations. The ability to scale from zero to hundreds of GPUs in a matter of minutes. NVIDIA's reliability, availability, and cost of GPUs. All aiding Peptone in AI-driven drug discovery.

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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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CoreWeave co-founder brannin mcbee reveals the A100 demand through 2029 is a big narrative violation for a market that thought GPUs would only have 2 to 3 years of useful life: "You don't need the latest generation GPU for every workload. You can think about it as a matrix of different sizes of models versus different sizes of GPUs, and it all just gets filled in volumetrically and economically." "That A100 GPU will cost less on a dollar per hour basis than a brand-new GB300 GPU. But the workload fits most efficiently on that A100, and it ultimately comes down to the engineering organizations making those requests." "It's been a pretty big narrative violation for the market that thought that these GPUs would only have two to three years of useful life, and instead we're contracting GPUs out through its ninth year of life. We expect to see that repeated across subsequent SKUs as well." CoreWeave

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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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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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"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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It’s time to put the Neocloud GPU depreciation debate to rest: $CRWV CEO: “Older generations of GPUs are going to have a longer useful life than anyone anticipated. They are going to contract for a longer term and at a higher price.” CoreWeave signed a fully priced A100 contract through 2029, a chip that came to market in 2020. Vintage GPUs are not dying. They’re getting contracted at full freight.

It’s time to put the Neocloud GPU depreciation debate to rest: $CRWV CEO: “Older generations of GPUs are going to have a longer useful life than anyone anticipated. They are going to contract for a longer term and at a higher price.” CoreWeave signed a fully priced A100 contract through 2029, a chip that came to market in 2020. Vintage GPUs are not dying. They’re getting contracted at full freight.

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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."

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 $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.

Bearly AI

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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.

Bearly AI

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