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We asked Vincent Weisser about his vision for utilizing GPUs. "Every idling GPU is a market failure. Compute will be one of the biggest slices of GDP." "The goal is to have a fault-tolerant 'genius pool' of compute, ranging from H100s, A100s to even RTX 3090s." "Unutilized GPUs are... show more

TBPN

1,097,322 subscribers

24,793 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 6

Фото профиля JJ

JJ1 год назад

@derekedws @vincentweisser Yes.

Фото профиля Dan Ryder - primedefi.com

Dan Ryder - primedefi.com1 год назад

DeFi’s momentum is just getting started. BTC staking is poised to become a $12B industry by 2025. Ethereum L2s are proving their profitability with skyrocketing gains. AI-powered tools are redefining the way trades are executed. The future belongs to those who stay ahead of the curve. What’s your top pick for the next big leap in DeFi?

Фото профиля Bridget

Bridget1 год назад

@vincentweisser 👏

Фото профиля 0xroyce - τ/Æ

0xroyce - τ/Æ1 год назад

@vincentweisser right guy:"what the hell is he talking about?" left guy:"why are you asking me? just keep saying yeah" vincent: "so let me just quickly squeeze all decentralised world and training into approx 150B tokens since I just got 420 H100s in my blood" brilliant here 😭

Фото профиля uncensored ai

uncensored ai1 год назад

@vincentweisser go @vincentweisser !

Фото профиля 𝕤𝕡/𝕒𝕔𝕔 | yangwao.hl

𝕤𝕡/𝕒𝕔𝕔 | yangwao.hl1 год назад

@vincentweisser GPUs on @tbpn, congrats @vincentweisser and @PrimeIntellect

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“There are more companies than ideas by quite a bit. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. AlexNet was built on 2 GPUs. The transformer was built on 8 to 64 GPUs. Which would be, what, 2 GPUs of today? You could argue that o1 reasoning was not the most compute heavy thing in the world. For research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute. If everyone is within the same paradigm, then compute becomes one of the big differentiators.” Ilya Sutskever

“There are more companies than ideas by quite a bit. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. AlexNet was built on 2 GPUs. The transformer was built on 8 to 64 GPUs. Which would be, what, 2 GPUs of today? You could argue that o1 reasoning was not the most compute heavy thing in the world. For research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute. If everyone is within the same paradigm, then compute becomes one of the big differentiators.” Ilya Sutskever

Dwarkesh Patel

203,605 просмотров • 8 месяцев назад

"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.

"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.

Exabits

13,895 просмотров • 1 год назад

ROBA Compute is officially LIVE. This isn't a feature drop. This is ROBA expanding into the AI infrastructure market - hundreds of billions in scale, and we're staking our claim. Rent GPUs on-demand. Enterprise-grade allocation. Transparent USDC pricing. Zero hidden fees and all on-chain. We're already in talks with enterprise clients for compute at scale. 15% of all compute revenue → straight to $ROBA buybacks & burns. GPU capacity is limited at launch. First come, first served. Over the next weeks we will begin accepting new compute partners to provide more power to the marketplace in addition to ROBA's own in-house GPUs and integrated robotics stack. Platform fees are discounted for a limited time only. The infrastructure layer powering physical AI and beyond starts here.

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Roba Labs

39,142 просмотров • 16 дней назад

We got Kostia of Inferra on the stream yesterday talking about his experience renting GPUs to render videos for his film production company to eventually building Inferra while participating in the EasyA Kickstart hackathon Inferra is building the exchange for GPU compute, turning raw graphics-card power into something you can trade and route like any liquid market.

We got Kostia of Inferra on the stream yesterday talking about his experience renting GPUs to render videos for his film production company to eventually building Inferra while participating in the EasyA Kickstart hackathon Inferra is building the exchange for GPU compute, turning raw graphics-card power into something you can trade and route like any liquid market.

gg.xyz

18,551 просмотров • 1 месяц назад

NVIDIA is about to grow a new moat that has the potential to be the company’s most impenetrable moat yet: The Compute Futures Market One of the reasons why there’s so much demand for U.S. T-bills is because the market for them is incredibly deep, and large pools of capital can come in and out freely without much disturbance. That attribute is attractive, which leads to more demand for T-bills Liquidity begets liquidity Another example is what Bill Ackman is describing in the attached clip - the more valuable a company becomes, the easier it is for said company to raise capital to fund expansion, which in of itself is virtuous and valuable The same concept is going to apply to compute, and in some ways- it already is. NVIDIA GPUs are already the most financeable (in many cases the only financeable) form of compute for various parties to deploy in their data centers. But the introduction of a forward curve that facilitates financial expression like hedging takes that concept to a different level Companies who consume compute (structurally short compute) will want to be able to hedge their input costs, and companies who produce compute (structurally long compute) will want to be able to hedge their output price. The market will coalesce even more around the deepest / most liquid pools to facilitate this - which will be NVIDIA based futures (WTI Crude) This liquidity itself will become part of the value of purchasing NVIDIA equipment, it will be part of the justification to pay the “Nvidia tax”. And when the liquidity reaches a certain depth, it will be almost impossible to break - because to break it would involve coordinating between massive amounts of misaligned parties- impossible So NVIDIA, by no additional virtue or effort of their own will likely absorb another massive structural advantage that may be the most difficult advantage to overcome for any competitor out of anything that has been built so far. The rich really do get richer.

NVIDIA is about to grow a new moat that has the potential to be the company’s most impenetrable moat yet: The Compute Futures Market One of the reasons why there’s so much demand for U.S. T-bills is because the market for them is incredibly deep, and large pools of capital can come in and out freely without much disturbance. That attribute is attractive, which leads to more demand for T-bills Liquidity begets liquidity Another example is what Bill Ackman is describing in the attached clip - the more valuable a company becomes, the easier it is for said company to raise capital to fund expansion, which in of itself is virtuous and valuable The same concept is going to apply to compute, and in some ways- it already is. NVIDIA GPUs are already the most financeable (in many cases the only financeable) form of compute for various parties to deploy in their data centers. But the introduction of a forward curve that facilitates financial expression like hedging takes that concept to a different level Companies who consume compute (structurally short compute) will want to be able to hedge their input costs, and companies who produce compute (structurally long compute) will want to be able to hedge their output price. The market will coalesce even more around the deepest / most liquid pools to facilitate this - which will be NVIDIA based futures (WTI Crude) This liquidity itself will become part of the value of purchasing NVIDIA equipment, it will be part of the justification to pay the “Nvidia tax”. And when the liquidity reaches a certain depth, it will be almost impossible to break - because to break it would involve coordinating between massive amounts of misaligned parties- impossible So NVIDIA, by no additional virtue or effort of their own will likely absorb another massive structural advantage that may be the most difficult advantage to overcome for any competitor out of anything that has been built so far. The rich really do get richer.

Nick Dorsey

33,944 просмотров • 18 дней назад

EXCLUSIVE: we asked Sriram Krishnan to break down the $600B deal announced last week by The White House. "We signed the first AI acceleration partnership. There are 3 critical components to this deal." "First, this represents a large investment in US data centers and US AI infrastructure. This is net new and means infrastructure will be built out physically in the US.” "Second, the vast majority of the GPUs, hosted in the UAE, will be run by American hyperscaler companies. This is an expansion opportunity for all of our companies. They will get to win market share away from competition from other countries.” "Third, security protocols around GPUs so they can't get physically diverted, especially to countries of concern." "We are getting resource-rich countries who are critical allies to adopt the American AI stack. This means they are going to be part of our ecosystem for decades to come, and it essentially forms a shield from them ever adopting tech or working with countries we don't want them to work with."

EXCLUSIVE: we asked Sriram Krishnan to break down the $600B deal announced last week by The White House. "We signed the first AI acceleration partnership. There are 3 critical components to this deal." "First, this represents a large investment in US data centers and US AI infrastructure. This is net new and means infrastructure will be built out physically in the US.” "Second, the vast majority of the GPUs, hosted in the UAE, will be run by American hyperscaler companies. This is an expansion opportunity for all of our companies. They will get to win market share away from competition from other countries.” "Third, security protocols around GPUs so they can't get physically diverted, especially to countries of concern." "We are getting resource-rich countries who are critical allies to adopt the American AI stack. This means they are going to be part of our ecosystem for decades to come, and it essentially forms a shield from them ever adopting tech or working with countries we don't want them to work with."

TBPN

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Billions of dollars in GPUs are sitting completely unused right now Greg Osuri, the founder of Akash, built the Airbnb of compute to put all those idle GPUs to work. "The Amazons, Googles of the world, they're like hotels, which are usually expensive.” "There is lots of compute that's unevenly distributed, owned by people, corporations, companies, that is supposedly dedicated only for them, but most of them don't use them."

Billions of dollars in GPUs are sitting completely unused right now Greg Osuri, the founder of Akash, built the Airbnb of compute to put all those idle GPUs to work. "The Amazons, Googles of the world, they're like hotels, which are usually expensive.” "There is lots of compute that's unevenly distributed, owned by people, corporations, companies, that is supposedly dedicated only for them, but most of them don't use them."

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Incoming: The Big Ole Chip Flip CPUs > GPUs COATUE CIO of Public Investments Jaimin Rangwalla says AI agents could fundamentally reshape compute demand: “Historically CPUs have done something called serial processing, which is like you just give it a set of instructions and it does one by one by one by one. It was very much a 1 CPU : 8 GPUs. It had gone up to 1 CPU : 16 GPUs, which was all of the math & all of the computation happening at the GPUs. But now the ratio is actually moving from 1 CPU : 4 GPUs."

Incoming: The Big Ole Chip Flip CPUs > GPUs COATUE CIO of Public Investments Jaimin Rangwalla says AI agents could fundamentally reshape compute demand: “Historically CPUs have done something called serial processing, which is like you just give it a set of instructions and it does one by one by one by one. It was very much a 1 CPU : 8 GPUs. It had gone up to 1 CPU : 16 GPUs, which was all of the math & all of the computation happening at the GPUs. But now the ratio is actually moving from 1 CPU : 4 GPUs."

Molly O’Shea

15,390 просмотров • 2 месяцев назад

If OpenAI actually pulls this off and builds billions of GPUs, every single human on Earth would basically have access to their own GPU. That’s the vision of Universal Basic Compute Sam Altman has been hinting at, compute as abundant and accessible as electricity. Not just AI for the few, but AI for everyone.

If OpenAI actually pulls this off and builds billions of GPUs, every single human on Earth would basically have access to their own GPU. That’s the vision of Universal Basic Compute Sam Altman has been hinting at, compute as abundant and accessible as electricity. Not just AI for the few, but AI for everyone.

VraserX e/acc

82,484 просмотров • 10 месяцев назад

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AI Compute —ubiquitous, invisible, and instant. Russ , Exabits Chief of Sales, on the future of compute: “You plug in your lamp, you get power. You don’t think about where it comes from. That’s our vision for GPUs—distributed, on-demand, and everywhere AI needs it.” Emerging providers like Exabits are the backbone of this shift—scaling infrastructure to meet global AI demand.

Exabits

55,256 просмотров • 1 год назад

Testing image-to-CAD for SR Platform on a new set of GPUs 🔥 We are working with lium.io (SN51) to showcase the efficiency of decentralized compute for SR Platform — from CAD generation to RL training and sim-to-real validation What it means: > Flexible, on-demand rental for us. Pay with crypto. > Diverse GPU options, powered by Openτensor Foundaτion's incentive alignment. > Lower equipment cost → higher margin for the network. Onchain robots, onchain compute.

Testing image-to-CAD for SR Platform on a new set of GPUs 🔥 We are working with lium.io (SN51) to showcase the efficiency of decentralized compute for SR Platform — from CAD generation to RL training and sim-to-real validation What it means: > Flexible, on-demand rental for us. Pay with crypto. > Diverse GPU options, powered by Openτensor Foundaτion's incentive alignment. > Lower equipment cost → higher margin for the network. Onchain robots, onchain compute.

Strike Robot

18,134 просмотров • 2 месяцев назад

Adaption Labs CEO Sara Hooker says data centers in space are "pretty bonkers", and explains what the current consensus gets wrong: "Most co-located hardware is pretty much for training. I think that's why you care [about Space], otherwise you can distribute. Inference compute, which is where everything's moving towards, you can spread that compute more easily. You can have multiple data centers [on Earth]." "So if you care about space, you probably only care about training compute. I think people underestimate the amount of failures that happen, and you don't want to get your training job interrupted." "The real issue is that GPUs still have failure rates. The 2% of GPUs that are just considered done every year, that's really your cost. It's how quickly you can replace those [in space] and what that looks like."

Adaption Labs CEO Sara Hooker says data centers in space are "pretty bonkers", and explains what the current consensus gets wrong: "Most co-located hardware is pretty much for training. I think that's why you care [about Space], otherwise you can distribute. Inference compute, which is where everything's moving towards, you can spread that compute more easily. You can have multiple data centers [on Earth]." "So if you care about space, you probably only care about training compute. I think people underestimate the amount of failures that happen, and you don't want to get your training job interrupted." "The real issue is that GPUs still have failure rates. The 2% of GPUs that are just considered done every year, that's really your cost. It's how quickly you can replace those [in space] and what that looks like."

TBPN

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We asked Tom Schmidt ＞|＜ from Dragonfly ＞|＜ about AI x crypto. "We are more bearish on this category." "A lot of AI crypto is broken down into people trying to do decentralized inference, this GPU marketplace. This is an old idea in crypto." "If you want to train a state-of-the-art model, you don't use 1M consumer-grade GPUs around the world. You want one big co-located A100 cluster." "The areas where we are more excited about are MPC and agent payments, where stablecoins are uniquely positioned to provide microtransactions."

We asked Tom Schmidt ＞|＜ from Dragonfly ＞|＜ about AI x crypto. "We are more bearish on this category." "A lot of AI crypto is broken down into people trying to do decentralized inference, this GPU marketplace. This is an old idea in crypto." "If you want to train a state-of-the-art model, you don't use 1M consumer-grade GPUs around the world. You want one big co-located A100 cluster." "The areas where we are more excited about are MPC and agent payments, where stablecoins are uniquely positioned to provide microtransactions."

TBPN

41,164 просмотров • 1 год назад

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."

TBPN

31,256 просмотров • 4 месяцев назад

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."

Invest Like the Best

211,336 просмотров • 2 месяцев назад

OpenAI President, Greg Brockman: We are moving to a world where GDP growth is driven by the amount of compute available openAI has tripled its compute annually, yet remains so compute-constrained "every time we want to launch something new, we fight over compute"

OpenAI President, Greg Brockman: We are moving to a world where GDP growth is driven by the amount of compute available openAI has tripled its compute annually, yet remains so compute-constrained "every time we want to launch something new, we fight over compute"

Haider.

64,739 просмотров • 6 месяцев назад

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

Sami Kassab

35,706 просмотров • 6 месяцев назад

Long Live Outputmaxxing: AI compute grids, Anthropic’s coding takeoff, data center backlash, & frontier systems AMP PBC founder Anjney Midha explains why 95% GPU utilization was considered an outage at Google, why the AI race is no longer just about buying more GPUs, how AMP is trying to make FLOPs flow like megawatts, why data center backlash could become one of AI’s biggest bottlenecks, how Anthropic cracked coding through culture and preparation, why DeepMind research hoarding creates a market failure, and why the next frontier may belong to teams that can “output max” across compute, capital, culture, and science.

Long Live Outputmaxxing: AI compute grids, Anthropic’s coding takeoff, data center backlash, & frontier systems AMP PBC founder Anjney Midha explains why 95% GPU utilization was considered an outage at Google, why the AI race is no longer just about buying more GPUs, how AMP is trying to make FLOPs flow like megawatts, why data center backlash could become one of AI’s biggest bottlenecks, how Anthropic cracked coding through culture and preparation, why DeepMind research hoarding creates a market failure, and why the next frontier may belong to teams that can “output max” across compute, capital, culture, and science.

Latent.Space

30,621 просмотров • 1 месяц назад