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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...

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Fixera AI

3,378 subscribers

26,682 просмотров • 1 год назад •via X (Twitter)

ФинансыНаука и технологииИгры#GPU
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Комментарии: 10

Фото профиля DonaXbτ 🐂🀄️
DonaXbτ 🐂🀄️1 год назад

Game changer 😎

Фото профиля The AltChemist
The AltChemist1 год назад

@IncomeSharks 10 utilities. Chart looks good long term too right?

Фото профиля Crypto_Jopp
Crypto_Jopp1 год назад

$CNCT 🫡

Фото профиля Joko
Joko1 год назад

This is an incredible new utility for $CNCT, they are on the way to mass adoption. Announcing a new utility, update one existing bot an announcing a new partnership in one! day, that’s how active teams are working 🔥

Фото профиля Ace ➰
Ace ➰1 год назад

ay ay ay $CNCT leveling up here Love to see the progress will be there when we hit 10M+ Marketcap as first goal. This project and team deserve much higher

Фото профиля Twilty 🥷🏼
Twilty 🥷🏼1 год назад

Huge. 👏 $CNCT

Фото профиля Fokatato
Fokatato1 год назад

This one is HUGE. $CNCT

Фото профиля Soma
Soma1 год назад

Big big $CNCT 🔥

Фото профиля Luca
Luca1 год назад

$CNCT 😈

Фото профиля Web3 Wordsmith
Web3 Wordsmith1 год назад

$CNCT, a blue chip! ⚡🔥 You really need to have a look at it @IncomeSharks

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We are excited to share a deeper look into the GPU Sharing Platform that will power AI applications on the InfraX V4 DApp. In this demo we showcase a GPU running and completing an image generation task. Our backend successfully leverages this GPU (and CPU) in real time to complete the job, earning the user money, and powering the infraX platform. Here’s how it works 👉 when you connect to the InfraX DApp, our platform automatically detects the GPU on your machine and immediately checks it eligibility and where it can be used. From there, each user dictates exactly how and when they want to contribute their GPU power by setting the days their PC is most available (throughout the night, for example). Additional, each user will be able to pause their GPU's connection to infraX whenever they need to use it locally, resuming with a just a single click. Once activated, your GPU seamlessly plugs into the InfraX network and begins fulfilling compute jobs, earning YOU money. Go on, take the $INFRA pill..
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We are excited to share a deeper look into the GPU Sharing Platform that will power AI applications on the InfraX V4 DApp. In this demo we showcase a GPU running and completing an image generation task. Our backend successfully leverages this GPU (and CPU) in real time to complete the job, earning the user money, and powering the infraX platform. Here’s how it works 👉 when you connect to the InfraX DApp, our platform automatically detects the GPU on your machine and immediately checks it eligibility and where it can be used. From there, each user dictates exactly how and when they want to contribute their GPU power by setting the days their PC is most available (throughout the night, for example). Additional, each user will be able to pause their GPU's connection to infraX whenever they need to use it locally, resuming with a just a single click. Once activated, your GPU seamlessly plugs into the InfraX network and begins fulfilling compute jobs, earning YOU money. Go on, take the $INFRA pill..

infraX | $INFRA

30,615 просмотров • 1 год назад

▶️ Solidus Ai Tech GPU Marketplace: First Look Video! 🌟 Introducing our GPU Marketplace preview! Access high-quality computing power economically from our cutting-edge HPC Data Center in Europe. Choose from a wide range of GPU options from global vendors with just a few clicks. 🔹Marketplace Offers: - Easy management of your resources - Choosing your GPU Instance - Customize your OS Template - Perpetual token burn using a portion of the profits on every transaction 🔸Benefits: - Part of AITECH Ecosystem - Economical and efficient - Streamlined processes - Reach a global audience - Maximize your returns - Deploy with a single click Solidus Ai Tech GPU Marketplace, ensures a swift, effortless, and cost-effective journey for both users and vendors. Our platform will provide seamless access to GPU power, real-time performance insights, and robust management tools. Join us to transform your computing experience.
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▶️ Solidus Ai Tech GPU Marketplace: First Look Video! 🌟 Introducing our GPU Marketplace preview! Access high-quality computing power economically from our cutting-edge HPC Data Center in Europe. Choose from a wide range of GPU options from global vendors with just a few clicks. 🔹Marketplace Offers: - Easy management of your resources - Choosing your GPU Instance - Customize your OS Template - Perpetual token burn using a portion of the profits on every transaction 🔸Benefits: - Part of AITECH Ecosystem - Economical and efficient - Streamlined processes - Reach a global audience - Maximize your returns - Deploy with a single click Solidus Ai Tech GPU Marketplace, ensures a swift, effortless, and cost-effective journey for both users and vendors. Our platform will provide seamless access to GPU power, real-time performance insights, and robust management tools. Join us to transform your computing experience.

AITECH CLOUD NETWORK

82,395 просмотров • 2 лет назад

AIRian is thrilled to announce a strategic partnership with io.net, a leading decentralized GPU compute platform. 🤝 This collaboration marks a significant milestone for AIRian as we embark on building the largest Web 3.0 running community. 🚀 With the support of we’ll be deploying and expanding GPU compute resources to power an active user community. AIRian’s application and platform will collect all the data people generate in their daily lives including walking, running, marathon, and healthcare related data and create extensive Sports-AI models focused on running behavior, statistics, etc. AIRian will also expand into GPS-based applications including competitions, Web3 games, and more. 🏃🏻🏃‍♂️🏃‍♀️ The frictionless access to GPU compute will allow us to easily accommodate our rollout and meet compute demands when we need it, as we need it. AIRian. Binance $AIR #AIRian #AIR #ionet #DePin #AI #SportsAI #dApp #socialFi
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AIRian is thrilled to announce a strategic partnership with io.net, a leading decentralized GPU compute platform. 🤝 This collaboration marks a significant milestone for AIRian as we embark on building the largest Web 3.0 running community. 🚀 With the support of we’ll be deploying and expanding GPU compute resources to power an active user community. AIRian’s application and platform will collect all the data people generate in their daily lives including walking, running, marathon, and healthcare related data and create extensive Sports-AI models focused on running behavior, statistics, etc. AIRian will also expand into GPS-based applications including competitions, Web3 games, and more. 🏃🏻🏃‍♂️🏃‍♀️ The frictionless access to GPU compute will allow us to easily accommodate our rollout and meet compute demands when we need it, as we need it. AIRian. Binance $AIR #AIRian #AIR #ionet #DePin #AI #SportsAI #dApp #socialFi

AIRian.

66,192 просмотров • 2 лет назад

Blendr's Beta Platform is Now Live 🖥 Our GPU Renting and Lending platform is officially live and you can use it now at Watch the tutorial video attached to this post to discover how to use the platform and read on to understand exactly how it works! 🔄 On-Demand Instance Creation Instead of keeping GPUs running 24/7 on the cloud, which can be very costly, AWS instances are created as and when they are requested to prevent unnecessary costs and inefficiency. 🔒 Secure Access A new set of SSH keys are generated for each instance for the user to download and login with. This ensures that only the current renter has access to their instance. 🔧 Instance Management Blendr’s Instance Manager service automatically terminates the instance after the rental period ends which ensures efficient use of resources. 📊 Transaction Tracking Users can view their transaction history through the dedicated Transaction Tab for ease of use and management. 💰 Lend Your Own GPU Power If you have spare GPU Capacity, you can join Blendr’s decentralization movement and leverage it to make some extra money! All you need to do is head to the Blendr CLI at to download our CLI tool, connect your GPU, and start contributing to the network. Dive in and utilise the power of decentralized GPU computing today!
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Blendr's Beta Platform is Now Live 🖥 Our GPU Renting and Lending platform is officially live and you can use it now at Watch the tutorial video attached to this post to discover how to use the platform and read on to understand exactly how it works! 🔄 On-Demand Instance Creation Instead of keeping GPUs running 24/7 on the cloud, which can be very costly, AWS instances are created as and when they are requested to prevent unnecessary costs and inefficiency. 🔒 Secure Access A new set of SSH keys are generated for each instance for the user to download and login with. This ensures that only the current renter has access to their instance. 🔧 Instance Management Blendr’s Instance Manager service automatically terminates the instance after the rental period ends which ensures efficient use of resources. 📊 Transaction Tracking Users can view their transaction history through the dedicated Transaction Tab for ease of use and management. 💰 Lend Your Own GPU Power If you have spare GPU Capacity, you can join Blendr’s decentralization movement and leverage it to make some extra money! All you need to do is head to the Blendr CLI at to download our CLI tool, connect your GPU, and start contributing to the network. Dive in and utilise the power of decentralized GPU computing today!

Blendr Network

130,462 просмотров • 2 лет назад

We're pleased to roll out a new feature at NetMind Power: the fiat payment option is now live! This addition allows users to easily rent GPUs using fiat currency, which our system then automatically converts into NetMind Token ( $NMT ) through a smart contract. It’s a step towards making our services more accessible to a wider audience, especially for those less familiar with crypto. Here’s a reminder of why choosing NetMind Power for your computing needs is a smart choice: • Economic Efficiency: Benefit from up to 90% savings on GPU compute costs • Flexible Refund Policy: Offering peace of mind, we provide a refund option for early terminations. • Easy Setup: Experience quick and efficient GPU selection and deployment. To see how the fiat payment option works, check out our video demonstration below. Dive into the world of NetMind Power with ease starting today!
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We're pleased to roll out a new feature at NetMind Power: the fiat payment option is now live! This addition allows users to easily rent GPUs using fiat currency, which our system then automatically converts into NetMind Token ( $NMT ) through a smart contract. It’s a step towards making our services more accessible to a wider audience, especially for those less familiar with crypto. Here’s a reminder of why choosing NetMind Power for your computing needs is a smart choice: • Economic Efficiency: Benefit from up to 90% savings on GPU compute costs • Flexible Refund Policy: Offering peace of mind, we provide a refund option for early terminations. • Easy Setup: Experience quick and efficient GPU selection and deployment. To see how the fiat payment option works, check out our video demonstration below. Dive into the world of NetMind Power with ease starting today!

NetMind.AI

33,472 просмотров • 2 лет назад

Dylan Patel of SemiAnalysis says a worse GPU with better storage and memory now beats the best chip without them, so buying the newest GPU alone no longer wins inference. So, an AMD GPU with more memory can outperform Nvidia in some cases. "So what we have is we have over $80 million of compute, GPUs from Nvidia, AMD, TPUs from Google, Trainium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest PyTorch version, whatever it is." "Every day it runs on an automated CI, and we run it on all the latest Chinese models, from GLM, Zhipu, Moonshot, Kimi, Alibaba, all these models we run." "Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it fixed context length." "But now with Agent X, we've analyzed over $5 million worth of Claude Code traces. This is real production traffic that people have donated to us as well as internally generated. Now we know what the actual agent workload looks like." "And then as we implement that and run those benchmarks, it turns out yes, the chip you're using is very important, but now even more important is how are you handling this memory offload?" "And so while an Nvidia GPU is faster than an AMD GPU in most cases, because AMD GPUs have more memory, they actually end up outperforming in some cases." "Or you can have a worse GPU, but a much better storage solution, and now you can outperform what the best GPU can do without those solutions. So just buying the newest and latest GPU alone doesn't get you the best inference economics." "Actually, you need to layer in all these other innovations including storage and memory." [ Who's the top player on your chart? ] "That really is a difficult multivariable problem. And generally that means you need to have, yes, you need to have the best GPU, a GB300, but you also need to have the best storage solutions. And so I won't spoil who's the best right here, but I will say that storage solutions matter a lot and memory solutions matter a lot, as does your front-end networking. That matters a lot."
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Dylan Patel of SemiAnalysis says a worse GPU with better storage and memory now beats the best chip without them, so buying the newest GPU alone no longer wins inference. So, an AMD GPU with more memory can outperform Nvidia in some cases. "So what we have is we have over $80 million of compute, GPUs from Nvidia, AMD, TPUs from Google, Trainium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest PyTorch version, whatever it is." "Every day it runs on an automated CI, and we run it on all the latest Chinese models, from GLM, Zhipu, Moonshot, Kimi, Alibaba, all these models we run." "Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it fixed context length." "But now with Agent X, we've analyzed over $5 million worth of Claude Code traces. This is real production traffic that people have donated to us as well as internally generated. Now we know what the actual agent workload looks like." "And then as we implement that and run those benchmarks, it turns out yes, the chip you're using is very important, but now even more important is how are you handling this memory offload?" "And so while an Nvidia GPU is faster than an AMD GPU in most cases, because AMD GPUs have more memory, they actually end up outperforming in some cases." "Or you can have a worse GPU, but a much better storage solution, and now you can outperform what the best GPU can do without those solutions. So just buying the newest and latest GPU alone doesn't get you the best inference economics." "Actually, you need to layer in all these other innovations including storage and memory." [ Who's the top player on your chart? ] "That really is a difficult multivariable problem. And generally that means you need to have, yes, you need to have the best GPU, a GB300, but you also need to have the best storage solutions. And so I won't spoil who's the best right here, but I will say that storage solutions matter a lot and memory solutions matter a lot, as does your front-end networking. That matters a lot."

Fireside Alpha

178,511 просмотров • 1 месяц назад

$NBIS is becoming one of the biggest winners in the new AI economy because it can deliver compute efficiently at scale. The number that mattered most to me was Nebius seeing four or more customers competing for every GPU it brings online (huge for AI cloud market). Management also said they raised prices again and are still selling out across both old and new GPU generations which is one of the cleanest pricing power signals you can get. This is a $100B company in the making.
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$NBIS is becoming one of the biggest winners in the new AI economy because it can deliver compute efficiently at scale. The number that mattered most to me was Nebius seeing four or more customers competing for every GPU it brings online (huge for AI cloud market). Management also said they raised prices again and are still selling out across both old and new GPU generations which is one of the cleanest pricing power signals you can get. This is a $100B company in the making.

Shay Boloor

589,512 просмотров • 4 месяцев назад

Dylan Patel on the importance of memory and storage Two key quotes: "An $NVDA GPU is faster than an $AMD GPU in most cases, but because AMD GPUs have more memory, they can outperform Nvidia in certain workloads." “It is a difficult, multivariable problem. Generally, you need the best GPU, such as a GB300, but you also need the best storage solutions. I will not spoil who comes out on top, but storage solutions matter a lot, memory solutions matter a lot, and frontend networking also matters significantly" Full Quote: “We have over $80 million of compute: GPUs from $NVDA and $AMD, TPUs from Google, and Trainium from Amazon. We constantly run this benchmark using the newest inference engines, drivers, PyTorch versions, and other software. It runs every day through automated CI across the latest Chinese models from GLM, Zhipu, Moonshot, Kimi, Alibaba, and others. Initially, when we were benchmarking the differences between these chips, inference engines, and parallelism schemes, we used fixed context lengths. But with Agent X, we have now analyzed more than $5 million worth of Claude Code traces. This is real production traffic that users have donated to us, combined with internally generated data, so we now understand what an actual agent workload looks like. When we implement those workloads and run the benchmarks, it turns out that the chip you are using is very important, but how you handle memory offload can be even more important. An Nvidia GPU is faster than an AMD GPU in most cases, but because AMD GPUs have more memory, they can outperform Nvidia in certain workloads. Similarly, you can use a less powerful GPU with a much better storage solution and outperform the best GPU when it lacks those solutions. Simply buying the newest GPU does not necessarily give you the best inference economics. You need to layer in other innovations, including storage and memory.” Interviewer: “Who is the top player on your chart? Can you tell us?” Dylan Patel: “It is a difficult, multivariable problem. Generally, you need the best GPU, such as a GB300, but you also need the best storage solutions. I will not spoil who comes out on top, but storage solutions matter a lot, memory solutions matter a lot, and frontend networking also matters significantly.”
2:27

Dylan Patel on the importance of memory and storage Two key quotes: "An $NVDA GPU is faster than an $AMD GPU in most cases, but because AMD GPUs have more memory, they can outperform Nvidia in certain workloads." “It is a difficult, multivariable problem. Generally, you need the best GPU, such as a GB300, but you also need the best storage solutions. I will not spoil who comes out on top, but storage solutions matter a lot, memory solutions matter a lot, and frontend networking also matters significantly" Full Quote: “We have over $80 million of compute: GPUs from $NVDA and $AMD, TPUs from Google, and Trainium from Amazon. We constantly run this benchmark using the newest inference engines, drivers, PyTorch versions, and other software. It runs every day through automated CI across the latest Chinese models from GLM, Zhipu, Moonshot, Kimi, Alibaba, and others. Initially, when we were benchmarking the differences between these chips, inference engines, and parallelism schemes, we used fixed context lengths. But with Agent X, we have now analyzed more than $5 million worth of Claude Code traces. This is real production traffic that users have donated to us, combined with internally generated data, so we now understand what an actual agent workload looks like. When we implement those workloads and run the benchmarks, it turns out that the chip you are using is very important, but how you handle memory offload can be even more important. An Nvidia GPU is faster than an AMD GPU in most cases, but because AMD GPUs have more memory, they can outperform Nvidia in certain workloads. Similarly, you can use a less powerful GPU with a much better storage solution and outperform the best GPU when it lacks those solutions. Simply buying the newest GPU does not necessarily give you the best inference economics. You need to layer in other innovations, including storage and memory.” Interviewer: “Who is the top player on your chart? Can you tell us?” Dylan Patel: “It is a difficult, multivariable problem. Generally, you need the best GPU, such as a GB300, but you also need the best storage solutions. I will not spoil who comes out on top, but storage solutions matter a lot, memory solutions matter a lot, and frontend networking also matters significantly.”

Daniel Romero

38,220 просмотров • 2 месяцев назад

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.
0:32

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.

Aethir

12,867 просмотров • 6 месяцев назад

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. $
0:35

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 просмотров • 2 месяцев 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.
0:41

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.

Roba Labs

44,287 просмотров • 1 месяц назад

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
1:03

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

MTS

11,699 просмотров • 27 дней назад

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!
0:56

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 просмотров • 2 лет назад

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.
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If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

Satya Nadella

2,024,764 просмотров • 11 месяцев назад

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!
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We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!

Jasper

161,741 просмотров • 1 год назад

Introducing KausaCompute. Running AI models privately is expensive and complicated. Traditional cloud providers require credit cards, KYC verification, and complex setup processes. For many developers, especially in emerging markets, accessing GPU compute remains out of reach. KausaCompute changes that. Deploy any Docker container with NVIDIA GPU, pay with USDC from Maze Pocket, no KYC, no credit card, no cloud provider account needed. GPU pricing starts at $0.47/hr with competitive rates across all tiers. KausaCompute lives inside KausaLayer Pocket. Open a pocket, swap SOL to USDC using the built-in swap feature, and start deploying GPU containers right away. Everything stays within one ecosystem. What it actually does: Private LLM endpoints. Deploy Llama 3, Mistral, CodeLlama, or any open-source model as a personal API. No one logs the prompts. No one reads the data. Full control. AI coding assistants that never see external servers. Speech-to-text processing for thousands of audio files in minutes instead of hours. Sentiment analysis across millions of data points. Document summarization at scale. Multi-modal AI that processes images and text together. All running on dedicated NVIDIA GPUs. A6000, T4, L4, L40, A100, H100, and more. Pick the hardware, pick the duration, deploy in one click. The billing is straightforward. USDC is deducted from Maze Pocket before deployment. Pro tip: Maze Pocket supports multiple pockets per wallet. Create a dedicated pocket just for KausaCompute to keep GPU spending separate from other activities like trading or transfers. Clean separation, easy tracking. KausaCompute is not another cloud provider. It is the fastest path from USDC to a running GPU, with zero identity requirements. Live now at
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Introducing KausaCompute. Running AI models privately is expensive and complicated. Traditional cloud providers require credit cards, KYC verification, and complex setup processes. For many developers, especially in emerging markets, accessing GPU compute remains out of reach. KausaCompute changes that. Deploy any Docker container with NVIDIA GPU, pay with USDC from Maze Pocket, no KYC, no credit card, no cloud provider account needed. GPU pricing starts at $0.47/hr with competitive rates across all tiers. KausaCompute lives inside KausaLayer Pocket. Open a pocket, swap SOL to USDC using the built-in swap feature, and start deploying GPU containers right away. Everything stays within one ecosystem. What it actually does: Private LLM endpoints. Deploy Llama 3, Mistral, CodeLlama, or any open-source model as a personal API. No one logs the prompts. No one reads the data. Full control. AI coding assistants that never see external servers. Speech-to-text processing for thousands of audio files in minutes instead of hours. Sentiment analysis across millions of data points. Document summarization at scale. Multi-modal AI that processes images and text together. All running on dedicated NVIDIA GPUs. A6000, T4, L4, L40, A100, H100, and more. Pick the hardware, pick the duration, deploy in one click. The billing is straightforward. USDC is deducted from Maze Pocket before deployment. Pro tip: Maze Pocket supports multiple pockets per wallet. Create a dedicated pocket just for KausaCompute to keep GPU spending separate from other activities like trading or transfers. Clean separation, easy tracking. KausaCompute is not another cloud provider. It is the fastest path from USDC to a running GPU, with zero identity requirements. Live now at

KausaLayer

10,945 просмотров • 3 месяцев назад