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NodeAI GPU Aggregator: One dashboard to access 40,000+ server-grade GPUs from 10+ providers. Spin up H100s, A100s and more in minutes. $GPU holders get 20–30% lower rates. Build faster, spend less. #AI #RWA

Node AI | $GPU

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62,653 views • 11 months ago •via X (Twitter)

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GPU Network Upgrade Incoming! We’re expanding to 100K+ server-grade GPUs via our new aggregator. - $GPU holders get deeper deployment discounts. - Stakers earn a larger share of the boosted revenue. Scale faster, earn more—update goes live soon! ⚡️ #AI #GPU #NodeAI

GPU Network Upgrade Incoming! We’re expanding to 100K+ server-grade GPUs via our new aggregator. - $GPU holders get deeper deployment discounts. - Stakers earn a larger share of the boosted revenue. Scale faster, earn more—update goes live soon! ⚡️ #AI #GPU #NodeAI

Node AI | $GPU

10,148 views • 1 year ago

Deploy a GPU node on NodeAI in minutes. 1. Head to 2. Choose a template (SSH, Oobabooga, more coming) 3. Select your GPU server 4. Hit Deploy and monitor from your dashboard Build faster. Ship smarter. #AI #GPU #NODEAI #DePIN

Deploy a GPU node on NodeAI in minutes. 1. Head to 2. Choose a template (SSH, Oobabooga, more coming) 3. Select your GPU server 4. Hit Deploy and monitor from your dashboard Build faster. Ship smarter. #AI #GPU #NODEAI #DePIN

Node AI | $GPU

26,514 views • 10 months ago

H100s, Blackwell & the GPU Revolution GPU supply is tight and demand is surging. We aggregate thousands of H100s, A100s, L40S, MI300X & more into one on-demand pool. One click and you’re training LLMs or vision models—no sticker shock. Skip the shortage. Build on NodeAI today. ⚡️ #AI #GPU #NODEAI

H100s, Blackwell & the GPU Revolution GPU supply is tight and demand is surging. We aggregate thousands of H100s, A100s, L40S, MI300X & more into one on-demand pool. One click and you’re training LLMs or vision models—no sticker shock. Skip the shortage. Build on NodeAI today. ⚡️ #AI #GPU #NODEAI

Node AI | $GPU

12,966 views • 1 year ago

🤖 AI agents need serious compute. 🌐 NodeAI brings it—100,000+ server-grade GPUs in one decentralized network. Train, deploy, and scale agents instantly, without bottlenecks or sticker shock. Power your AI with NodeAI. ⚡️ #NodeAI #GPU #AI

🤖 AI agents need serious compute. 🌐 NodeAI brings it—100,000+ server-grade GPUs in one decentralized network. Train, deploy, and scale agents instantly, without bottlenecks or sticker shock. Power your AI with NodeAI. ⚡️ #NodeAI #GPU #AI

Node AI | $GPU

10,071 views • 1 year ago

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 missed opportunities for technological progress like curing diseases. The goal is to get to 100% utilization and we're far from it."

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 missed opportunities for technological progress like curing diseases. The goal is to get to 100% utilization and we're far from it."

TBPN

24,793 views • 1 year ago

Launching Now: On-Demand Access to H100s,5090, H200s, B200s & more No more waiting lists. No long-term contracts. No vendor lock-ins. Just instant access to the GPUs you need—from SXM5 InfiniBand to PCIe—all in one place. ⚡ Spin up 100+ H100s or reserve a few H200s for training 🧠 Choose across providers without switching tabs 💸 Pay only when you run workloads Your infra team’s dream stack is live. Fire up your first cluster now

Launching Now: On-Demand Access to H100s,5090, H200s, B200s & more No more waiting lists. No long-term contracts. No vendor lock-ins. Just instant access to the GPUs you need—from SXM5 InfiniBand to PCIe—all in one place. ⚡ Spin up 100+ H100s or reserve a few H200s for training 🧠 Choose across providers without switching tabs 💸 Pay only when you run workloads Your infra team’s dream stack is live. Fire up your first cluster now

Spheron AI

10,899 views • 1 year ago

⚡️ NEW: Nosana launches AI Grants. Up to $50K for startups, researchers, and solo builders working on what’s next in AI. Selected teams also get free GPU compute credits, cutting infrastructure costs so you can move faster from idea to deployment. Nosana operates a global GPU marketplace focused on accessible and affordable compute. [Brought to you by Nosana]

⚡️ NEW: Nosana launches AI Grants. Up to $50K for startups, researchers, and solo builders working on what’s next in AI. Selected teams also get free GPU compute credits, cutting infrastructure costs so you can move faster from idea to deployment. Nosana operates a global GPU marketplace focused on accessible and affordable compute. [Brought to you by Nosana]

Cointelegraph

58,396 views • 7 months ago

We’re excited to introduce the new Paystack Dashboard, rebuilt from the ground up for the first time in 10 years🚀 The new Dashboard is faster, simpler, mobile-ready, and AI-native, designed to help businesses understand what’s happening and get more done. 🧵👇🏿

We’re excited to introduce the new Paystack Dashboard, rebuilt from the ground up for the first time in 10 years🚀 The new Dashboard is faster, simpler, mobile-ready, and AI-native, designed to help businesses understand what’s happening and get more done. 🧵👇🏿

Paystack

91,208 views • 2 months ago

Revolutionizing Access to #GPU Power - One Node at a Time.⚡️ Our 10th Utility is coming out on June 20.🚨 BIG GPU Nodes Platform isn’t just another name in the crowded GPU cloud services space, we’re a game-changer. Our platform is purpose-built to bridge the gap between server owners and those hungry for raw, high-performance computing power. 🖥️ We specialize in connecting suppliers from individual server owners to enterprise-scale GPU rig operators with consumers who demand serious horsepower for AI, machine learning, data science, and other compute-heavy applications. Whether you’re offering a sleek single-GPU setup or a massive mining-style array with multiple GPUs, BIG GPU Nodes is your launchpad. We make it simple to rent out underutilized GPU resources and just as easy for users to find exactly what they need — fast, affordable, and tailored to their workload.⛓️‍💥 This is more than cloud computing. This is decentralized GPU power for the future of innovation. ♾️ This product will generate revenue and we will give back to our loyal $CNCT holders. ✨ We’re proud to have MESSIER | M87 as our trusted partner and advisor, backing us with this vision. #M87 🤝 $CNCT From the very beginning, we promised you ONE BIG ECOSYSTEM, and a seamless space where everything you need comes together. Now, that vision is becoming reality. Millions of users will soon be connected, working, creating, and thriving within it. 🌍

Revolutionizing Access to #GPU Power - One Node at a Time.⚡️ Our 10th Utility is coming out on June 20.🚨 BIG GPU Nodes Platform isn’t just another name in the crowded GPU cloud services space, we’re a game-changer. Our platform is purpose-built to bridge the gap between server owners and those hungry for raw, high-performance computing power. 🖥️ We specialize in connecting suppliers from individual server owners to enterprise-scale GPU rig operators with consumers who demand serious horsepower for AI, machine learning, data science, and other compute-heavy applications. Whether you’re offering a sleek single-GPU setup or a massive mining-style array with multiple GPUs, BIG GPU Nodes is your launchpad. We make it simple to rent out underutilized GPU resources and just as easy for users to find exactly what they need — fast, affordable, and tailored to their workload.⛓️‍💥 This is more than cloud computing. This is decentralized GPU power for the future of innovation. ♾️ This product will generate revenue and we will give back to our loyal $CNCT holders. ✨ We’re proud to have MESSIER | M87 as our trusted partner and advisor, backing us with this vision. #M87 🤝 $CNCT From the very beginning, we promised you ONE BIG ECOSYSTEM, and a seamless space where everything you need comes together. Now, that vision is becoming reality. Millions of users will soon be connected, working, creating, and thriving within it. 🌍

Fixera AI

26,669 views • 1 year ago

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

87,151 views • 2 years ago

THIS CHINESE BUILDER TURNED 8 RETIRED TESLA P40s INTO A 192GB PRIVATE AI SERVER THAT CAN WORK 5,760 GPU-HOURS EVERY MONTH. 00:06 he holds up one used Tesla P40 while seven more sit behind it, then installs the full stack inside a massive dual-Xeon server chassis. each card carries 24GB of VRAM, giving the system 192GB across eight GPUs for local models, video processing, transcription, private documents and AI agents. the real advantage is parallel work. some cards can serve a local model while the rest generate images, process files and run overnight automation at the same time. eight GPUs running continuously deliver 5,760 GPU-hours every 30 days. rent that workload from the cloud and the meter keeps moving for every minute the server stays online. a $412 monthly AI stack already costs $4,944 a year before serious GPU rental. he bought retired hardware once, kept the data local and built a machine that keeps working after the browser closes.

THIS CHINESE BUILDER TURNED 8 RETIRED TESLA P40s INTO A 192GB PRIVATE AI SERVER THAT CAN WORK 5,760 GPU-HOURS EVERY MONTH. 00:06 he holds up one used Tesla P40 while seven more sit behind it, then installs the full stack inside a massive dual-Xeon server chassis. each card carries 24GB of VRAM, giving the system 192GB across eight GPUs for local models, video processing, transcription, private documents and AI agents. the real advantage is parallel work. some cards can serve a local model while the rest generate images, process files and run overnight automation at the same time. eight GPUs running continuously deliver 5,760 GPU-hours every 30 days. rent that workload from the cloud and the meter keeps moving for every minute the server stays online. a $412 monthly AI stack already costs $4,944 a year before serious GPU rental. he bought retired hardware once, kept the data local and built a machine that keeps working after the browser closes.

Gipp 🦅

42,332 views • 24 days ago

$NVDA is entering the consumer PC chip market with RTX Spark, an $ARM based CPU/GPU/AI chip for Windows laptops and mini-PCs. The flagship version includes 20 CPU cores, 6,144 GPU cores, and up to 128GB of unified LPDDR5X memory, aimed at local AI agents, creators, developers, and gaming. Nvidia says 30+ laptops and 10+ desktops are already in development with partners including Microsoft, Dell, HP, Asus, Lenovo, MSI, Acer, and Gigabyte.

$NVDA is entering the consumer PC chip market with RTX Spark, an $ARM based CPU/GPU/AI chip for Windows laptops and mini-PCs. The flagship version includes 20 CPU cores, 6,144 GPU cores, and up to 128GB of unified LPDDR5X memory, aimed at local AI agents, creators, developers, and gaming. Nvidia says 30+ laptops and 10+ desktops are already in development with partners including Microsoft, Dell, HP, Asus, Lenovo, MSI, Acer, and Gigabyte.

Wall St Engine

108,823 views • 1 month ago

Hey everyone, today I want to introduce a project that’s aiming to redefine how we access compute for AI — it’s called GPUAI. 🔶 GPUAI: Unlocking Global GPU Power for the AI Era GPUAI isn’t just another GPU marketplace or leasing service. It’s a fully decentralized protocol that connects idle GPU resources around the world — from gaming PCs to data center clusters — and transforms them into a high-performance compute network for AI workloads. 🧠 Why does it matter? Right now, the biggest bottleneck in AI isn’t algorithms — it’s access to compute. Training and running models requires massive GPU power, but it’s locked up in centralized cloud platforms, expensive and hard to access for smaller teams. With GPUAI, anyone can tap into a global GPU pool that’s: ✅ Fully decentralized ✅ Reputation-based and smart contract coordinated ✅ Encrypted and secure ✅ Token-incentivized — meaning contributors get rewarded in $GPUAI 📈 For developers, it’s a flexible way to access GPU compute for training, inference, and more — without cloud lock-in. 💰 For GPU owners, it’s a chance to monetize idle hardware that would otherwise go unused. The protocol is live, the apps are active, and the ecosystem is growing fast. 🌐 Try it yourself at 📖 Learn more on 🎮 Play our community games at This is real infrastructure for the future of AI, not hype. Follow them and explore their mission of decentralized computing at Tell me what you think - if you have a GPU, you can start profiting now. #GPUAI #Web3Infrastructure #AIComputing #DePIN #Decentralization

Hey everyone, today I want to introduce a project that’s aiming to redefine how we access compute for AI — it’s called GPUAI. 🔶 GPUAI: Unlocking Global GPU Power for the AI Era GPUAI isn’t just another GPU marketplace or leasing service. It’s a fully decentralized protocol that connects idle GPU resources around the world — from gaming PCs to data center clusters — and transforms them into a high-performance compute network for AI workloads. 🧠 Why does it matter? Right now, the biggest bottleneck in AI isn’t algorithms — it’s access to compute. Training and running models requires massive GPU power, but it’s locked up in centralized cloud platforms, expensive and hard to access for smaller teams. With GPUAI, anyone can tap into a global GPU pool that’s: ✅ Fully decentralized ✅ Reputation-based and smart contract coordinated ✅ Encrypted and secure ✅ Token-incentivized — meaning contributors get rewarded in $GPUAI 📈 For developers, it’s a flexible way to access GPU compute for training, inference, and more — without cloud lock-in. 💰 For GPU owners, it’s a chance to monetize idle hardware that would otherwise go unused. The protocol is live, the apps are active, and the ecosystem is growing fast. 🌐 Try it yourself at 📖 Learn more on 🎮 Play our community games at This is real infrastructure for the future of AI, not hype. Follow them and explore their mission of decentralized computing at Tell me what you think - if you have a GPU, you can start profiting now. #GPUAI #Web3Infrastructure #AIComputing #DePIN #Decentralization

The Crypto GEMs

69,984 views • 1 year ago

$Access codes will go out tomorrow via welcome emails. API status updates and full API access will follow on Friday. HdeAPoHivsm9MZfeY5tW7apJEprc8Fs594bWmnzfpump The $ICPX website has been rebuilt from the ground up and it reflects exactly where this protocol is headed. The new site is the gateway to decentralised compute powered by Nvidia and Google GPU cloud partner programs, meaning whenever you rent a GPU for a job, it is always available. No downtime, no waitlists. Enterprise grade H100s, H200s and RTX 4090s. Accessible to anyone, at a fraction of what centralised providers charge. Pay with $ICPX tokens and get immediate access to serious compute power. Stake $ICPX to unlock deeper discounts and watch your costs drop over time the more committed you are to the network. This is what pay-per-job decentralised compute actually looks like in practice. Full control over training and running your models, with a lean 0.25% protocol fee managed by multisig. Open-source, immutable, and formally verified with Kani. $$

Access codes will go out tomorrow via welcome emails. API status updates and full API access will follow on Friday. HdeAPoHivsm9MZfeY5tW7apJEprc8Fs594bWmnzfpump The $ICPX website has been rebuilt from the ground up and it reflects exactly where this protocol is headed. The new site is the gateway to decentralised compute powered by Nvidia and Google GPU cloud partner programs, meaning whenever you rent a GPU for a job, it is always available. No downtime, no waitlists. Enterprise grade H100s, H200s and RTX 4090s. Accessible to anyone, at a fraction of what centralised providers charge. Pay with $ICPX tokens and get immediate access to serious compute power. Stake $ICPX to unlock deeper discounts and watch your costs drop over time the more committed you are to the network. This is what pay-per-job decentralised compute actually looks like in practice. Full control over training and running your models, with a lean 0.25% protocol fee managed by multisig. Open-source, immutable, and formally verified with Kani. $

ICPX Protocol

32,161 views • 1 month ago

$0.05 PER KWH IN PARAGUAY VS $0.15 IN THE US, A 5,000 GPU FARM NEAR ASUNCION CLEARS $4.7M/MO ON VAST AI WHILE US OPERATORS NET $1.8M ON THE SAME RACK the warehouse in the clip is somewhere outside asuncion, multi story racks running floor to ceiling, the operator built it next to a hydroelectric dam where electricity costs 5 cents per kilowatt hour a US warehouse running the same 5,000 GPU rack burns through $1.6M a month in electricity at $0.15/kWh, the same rack in paraguay burns $530K, the gap is $1M of pure margin every 30 days the racks host RTX 4090s, A100s and H100s on vast ai and clore ai, indie AI startups and image generation services rent them by the hour because aws is rationing GPU access to fortune 500 a 4090 nets the US operator $500 to $1,000 a month, the paraguay operator clears $850 to $1,400 on the same card because power eats half what it does north of the equator bitfarms, riot and core scientific have all expanded into paraguay since 2024, the next 10,000 GPU AI farm coming online in 2026 is more likely to sit near a paraguayan dam than in texas bookmark this and read the article below

$0.05 PER KWH IN PARAGUAY VS $0.15 IN THE US, A 5,000 GPU FARM NEAR ASUNCION CLEARS $4.7M/MO ON VAST AI WHILE US OPERATORS NET $1.8M ON THE SAME RACK the warehouse in the clip is somewhere outside asuncion, multi story racks running floor to ceiling, the operator built it next to a hydroelectric dam where electricity costs 5 cents per kilowatt hour a US warehouse running the same 5,000 GPU rack burns through $1.6M a month in electricity at $0.15/kWh, the same rack in paraguay burns $530K, the gap is $1M of pure margin every 30 days the racks host RTX 4090s, A100s and H100s on vast ai and clore ai, indie AI startups and image generation services rent them by the hour because aws is rationing GPU access to fortune 500 a 4090 nets the US operator $500 to $1,000 a month, the paraguay operator clears $850 to $1,400 on the same card because power eats half what it does north of the equator bitfarms, riot and core scientific have all expanded into paraguay since 2024, the next 10,000 GPU AI farm coming online in 2026 is more likely to sit near a paraguayan dam than in texas bookmark this and read the article below

starmex

26,916 views • 1 month ago

PowerInfer - a high-speed inference engine for deploying LLMs locally. Just came across this super interesting project on speeding up inference. It's not MoE but it's a simple approach that exploits the high locality in LLM inference to design a GPU-CPU hybrid inference engine. Hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons (the majority) are computed on the CPU. This approach significantly reduces GPU memory demands and CPU-GPU data transfer. It achieves an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs on a single NVIDIA RTX 4090 GPU. It's on only 18% lower than that achieved by a top-tier server-grade A100 GPU. It also significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy. There is a lot more innovation around inference that's coming fast. Really encouraged by the study on sparse computation to enhance the computational efficiency of LLMs. It's now possible to use PowerInfer with Llama 2 and Faclon 40B. Mistral-7B support is coming soon!

PowerInfer - a high-speed inference engine for deploying LLMs locally. Just came across this super interesting project on speeding up inference. It's not MoE but it's a simple approach that exploits the high locality in LLM inference to design a GPU-CPU hybrid inference engine. Hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons (the majority) are computed on the CPU. This approach significantly reduces GPU memory demands and CPU-GPU data transfer. It achieves an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs on a single NVIDIA RTX 4090 GPU. It's on only 18% lower than that achieved by a top-tier server-grade A100 GPU. It also significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy. There is a lot more innovation around inference that's coming fast. Really encouraged by the study on sparse computation to enhance the computational efficiency of LLMs. It's now possible to use PowerInfer with Llama 2 and Faclon 40B. Mistral-7B support is coming soon!

elvis

261,622 views • 2 years ago

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 views • 2 months ago

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.

Fireside Alpha

38,370 views • 24 days ago

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 views • 1 year ago