Загрузка видео...

Не удалось загрузить видео

На главную

As AI apps become modular and dynamic, AI needs globally distributed, elastic, and affordable GPU compute that can spin up anywhere, anytime. AI applications won’t run on a single cloud. ✅They will run on distributed compute rails. That’s the future Aethir is building.

AethirCloud's profile picture

Aethir

720,101 subscribers

13,296 просмотров • 5 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Every AI response needs a GPU to run it. Every robot decision needs compute to power it. Every autonomous agent needs infrastructure to live on. We're building that infrastructure. ROBA Compute. Coming soon.
0:48

Every AI response needs a GPU to run it. Every robot decision needs compute to power it. Every autonomous agent needs infrastructure to live on. We're building that infrastructure. ROBA Compute. Coming soon.

Roba Labs

49,108 просмотров • 1 месяц назад

The future of AI deployment should be fast, affordable, and owned by all. Introducing Gradient Cloud, the all-in-one station for building with AI, powered by the Gradient distributed AI stack. Run frontier models at production speed for a fraction of the cost.
0:30

The future of AI deployment should be fast, affordable, and owned by all. Introducing Gradient Cloud, the all-in-one station for building with AI, powered by the Gradient distributed AI stack. Run frontier models at production speed for a fraction of the cost.

Gradient

128,284 просмотров • 9 месяцев назад

At #RenderCon Jules Urbach shared a view for the future of AI and decentralized compute, discussing how the industry is shifting from focusing on pre-training models towards inference time AI applications and agents that can run on The Render Network high performance consumer GPUs.
3:15

At #RenderCon Jules Urbach shared a view for the future of AI and decentralized compute, discussing how the industry is shifting from focusing on pre-training models towards inference time AI applications and agents that can run on The Render Network high performance consumer GPUs.

The Render Network

10,904 просмотров • 1 год назад

$GPU is about to become the backbone of decentralized AI compute | Node AI | $GPU
0:59

$GPU is about to become the backbone of decentralized AI compute | Node AI | $GPU

Investor Jordan 🌪️

55,563 просмотров • 11 месяцев назад

The future of AI compute doesn't run on hype. It runs on real utility, real burns, and a network that gets stronger every time it's used. The Incentive Dynamic Engine (IDE) is now live!
0:31

The future of AI compute doesn't run on hype. It runs on real utility, real burns, and a network that gets stronger every time it's used. The Incentive Dynamic Engine (IDE) is now live!

io.net

9,777,847 просмотров • 17 дней назад

Building the world's largest AI compute DePIN means has tens of thousands of independent node operators around the world. Providing alternatives to centralized cloud providers is more than bringing transactions for compute on-chain, it is tapping into the distributed compute that already exists in the world and making it available to innovators and builders advancing AI and ML. video from our community member leeland アブス ✳️
0:45

Building the world's largest AI compute DePIN means has tens of thousands of independent node operators around the world. Providing alternatives to centralized cloud providers is more than bringing transactions for compute on-chain, it is tapping into the distributed compute that already exists in the world and making it available to innovators and builders advancing AI and ML. video from our community member leeland アブス ✳️

io.net

962,209 просмотров • 2 лет назад

Tuning AI models is an increasingly important task — and where DePIN thrives. As Emad discussed at #RenderCon, the future of AI relies on distributed compute platforms like The Render Network to tune, scale, and personalize agents across industries.
0:32

Tuning AI models is an increasingly important task — and where DePIN thrives. As Emad discussed at #RenderCon, the future of AI relies on distributed compute platforms like The Render Network to tune, scale, and personalize agents across industries.

The Render Network

16,764 просмотров • 1 год назад

💨 AI shouldn’t be slowed down by its own infrastructure. Introducing Cottonia — a smarter, lighter cloud acceleration layer for AI. We use distributed computing and intelligent scheduling to make AI execution faster & cheaper. ♻️The future of intelligent compute is here.
2:17

💨 AI shouldn’t be slowed down by its own infrastructure. Introducing Cottonia — a smarter, lighter cloud acceleration layer for AI. We use distributed computing and intelligent scheduling to make AI execution faster & cheaper. ♻️The future of intelligent compute is here.

Cottonia

13,668 просмотров • 4 месяцев назад

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.
0:43

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 • 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!
2:18

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

OpenLedger is powering decentralized AI inference with DGrid AI, built on verifiable, onchain infrastructure. DGrid routes AI workloads across a distributed compute network, while OpenLedger anchors execution, attribution, and settlement fully onchain. Together, this enables AI systems that are auditable by design, transparent in cost and performance, and built for real Web3 applications.
0:22

OpenLedger is powering decentralized AI inference with DGrid AI, built on verifiable, onchain infrastructure. DGrid routes AI workloads across a distributed compute network, while OpenLedger anchors execution, attribution, and settlement fully onchain. Together, this enables AI systems that are auditable by design, transparent in cost and performance, and built for real Web3 applications.

OpenLedger

196,328 просмотров • 5 месяцев назад

Ecosystem Partner Spotlight: IoTeX ⬡ Today, we’re shining the spotlight on IoTeX, a key force inside the AI Unbundled Alliance. Aethir provides decentralized GPU compute, while IoTeX delivers DePIN AI blockchain infrastructure for real-world applications. 🧵👇🏻
0:24

Ecosystem Partner Spotlight: IoTeX ⬡ Today, we’re shining the spotlight on IoTeX, a key force inside the AI Unbundled Alliance. Aethir provides decentralized GPU compute, while IoTeX delivers DePIN AI blockchain infrastructure for real-world applications. 🧵👇🏻

Aethir Ecosystem

24,460 просмотров • 9 месяцев назад

⚡️ 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]
1:25

⚡️ 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

53,244 просмотров • 6 месяцев назад

As AI continues to skyrocket, energy demand will only grow 📈 Traditional hyperscalers scale vertically: more servers, more cooling, more energy. At Aethir, we’re taking a smarter path → scaling horizontally. Our decentralized GPU cloud distributes workloads globally, reducing inefficiencies, easing energy concentration, & building a sustainable compute network.
0:31

As AI continues to skyrocket, energy demand will only grow 📈 Traditional hyperscalers scale vertically: more servers, more cooling, more energy. At Aethir, we’re taking a smarter path → scaling horizontally. Our decentralized GPU cloud distributes workloads globally, reducing inefficiencies, easing energy concentration, & building a sustainable compute network.

Aethir

12,306 просмотров • 8 месяцев назад

⚡Why Edge Compute Matters for the Future of Agentic AI AI isn’t just about smarter models, it’s about where they run. Centralized clouds are costly, slow, and controlled by a few. That’s not the future. 👉Join the OptimAI Network: With OptimAI Edge Compute and the upcoming Core Node release, every device becomes part of a decentralized supercomputer: + AI models run directly on your devices (phones, desktops, IoT devices) + Data stays local, secure, and real-time + Compute scales with the community, not corporations This is how we power the next generation of Agentic AI - AI systems that don’t just think, but act like you. Lightweight, fast, everywhere, for everyone. Stay tuned. The Edge is coming.
0:29

⚡Why Edge Compute Matters for the Future of Agentic AI AI isn’t just about smarter models, it’s about where they run. Centralized clouds are costly, slow, and controlled by a few. That’s not the future. 👉Join the OptimAI Network: With OptimAI Edge Compute and the upcoming Core Node release, every device becomes part of a decentralized supercomputer: + AI models run directly on your devices (phones, desktops, IoT devices) + Data stays local, secure, and real-time + Compute scales with the community, not corporations This is how we power the next generation of Agentic AI - AI systems that don’t just think, but act like you. Lightweight, fast, everywhere, for everyone. Stay tuned. The Edge is coming.

OptimAI Network

43,576 просмотров • 9 месяцев назад

The Future of AI Compute is Here! AI runs on compute power—but there's a crisis. Enter Exabits, the AI Compute Refinery. Optimized, scalable, & powered by $EXA. Own the infrastructure behind the AI revolution. Watch now! 🎥
3:16

The Future of AI Compute is Here! AI runs on compute power—but there's a crisis. Enter Exabits, the AI Compute Refinery. Optimized, scalable, & powered by $EXA. Own the infrastructure behind the AI revolution. Watch now! 🎥

Exabits

359,053 просмотров • 1 год назад

🔥 NVIDIA GTC 2026: The Vera Rubin platform just dropped, introducing 10x inference throughput per watt, at 1/10th the cost per token vs. Blackwell. Jensen Huang called it: every company will now run a "token factory." Here's the thing: Aethir has been running distributed token factories since day one, with nearly 440,000+ GPU containers across 94 countries, delivering 1.5B+ compute hours to enterprises, powered by ATH. When the world's most powerful AI chips land, you need a cloud that can actually deploy them at scale, without the centralized cloud limitations and bottlenecks. That's Aethir’s advantage over centralized compute providers. ⚡
0:36

🔥 NVIDIA GTC 2026: The Vera Rubin platform just dropped, introducing 10x inference throughput per watt, at 1/10th the cost per token vs. Blackwell. Jensen Huang called it: every company will now run a "token factory." Here's the thing: Aethir has been running distributed token factories since day one, with nearly 440,000+ GPU containers across 94 countries, delivering 1.5B+ compute hours to enterprises, powered by ATH. When the world's most powerful AI chips land, you need a cloud that can actually deploy them at scale, without the centralized cloud limitations and bottlenecks. That's Aethir’s advantage over centralized compute providers. ⚡

Aethir Ecosystem

13,644 просмотров • 3 месяцев назад

building ai shouldn’t require burning through cloud credits. introducing Primis Protocol, affordable compute funded by onchain capital. the $primis
9:30

building ai shouldn’t require burning through cloud credits. introducing Primis Protocol, affordable compute funded by onchain capital. the $primis

Khouba

29,633 просмотров • 5 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.
0:32

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

81,421 просмотров • 9 месяцев назад

For anyone trying to understand Bittensor from first principles, this lecture is a useful place to start. Presented by Bittensor co-founder const. Learn Bittensor > Start with Bitcoin, distributed systems, incentives, > How Bitcoin leads to Bittensor Subnets coordinating AI infrastructure. Topics: // Start - Bitcoin as more than a digital currency // Risks of AI centralization + closed systems // "The incentive computer" // How Bittensor subnets work (mining, validating) // How distributed AI infrastructure could scale globally // Impact on students, builders & future founders Recorded at the National University of Singapore Computer Science Club. NUS Computing Chapters - Bitcoin, AI, and Bittensor - Bitcoin history and decentralization - AI changes how engineers work - The danger of centralized AI power - Why most crypto visions fail - Bitcoin as the world’s largest compute network - Bitcoin as a market for compute - The idea of an “incentive computer” - Bitcoin compared to Bittensor - Classroom example of decentralized scoring - A simple subnet example - SN62 :: Ridges AI | SN62 SWE agents - SN3 templar :: Distributed AI Training - SN52 lium.io :: GPU rentals on Bittensor 128 subnets, some examples Why this matters for the future of work Q&A Subnet examples mentioned @ SN64 - Serverless + TEE Compute :: Chutes SN8 - Prop firm Vanta Trading SN52 - AutoML :: Gradients SN62 - SWE agents :: Ridges AI | SN62 SN51 - Compute / GPU rental lium.io SN4 - TEE compute for enterprise :: Targon SN3 - 72B Distributed Training run :: templar SN41 - Prediction markets :: Almanac SN44 - Computer Vision Score - Subnet 44 SN68 - Drug discovery :: METANOVA SN18 - Weather Forecasting Zeus | SN 18 SN50 - Bitcoin prediction data :: Synthdata SN61 - Quantum computing :: qBitTensor Labs SN14 - Bitcoin mining pool :: TaoHash SN34 - Perp Dex :: 0xMarkets SN17 - 3D model generation :: 404 SN33 - Data analytics :: ReadyAI SN19 - [Since relaunched] RPC infrastructure :
53:45

For anyone trying to understand Bittensor from first principles, this lecture is a useful place to start. Presented by Bittensor co-founder const. Learn Bittensor > Start with Bitcoin, distributed systems, incentives, > How Bitcoin leads to Bittensor Subnets coordinating AI infrastructure. Topics: // Start - Bitcoin as more than a digital currency // Risks of AI centralization + closed systems // "The incentive computer" // How Bittensor subnets work (mining, validating) // How distributed AI infrastructure could scale globally // Impact on students, builders & future founders Recorded at the National University of Singapore Computer Science Club. NUS Computing Chapters - Bitcoin, AI, and Bittensor - Bitcoin history and decentralization - AI changes how engineers work - The danger of centralized AI power - Why most crypto visions fail - Bitcoin as the world’s largest compute network - Bitcoin as a market for compute - The idea of an “incentive computer” - Bitcoin compared to Bittensor - Classroom example of decentralized scoring - A simple subnet example - SN62 :: Ridges AI | SN62 SWE agents - SN3 templar :: Distributed AI Training - SN52 lium.io :: GPU rentals on Bittensor 128 subnets, some examples Why this matters for the future of work Q&A Subnet examples mentioned @ SN64 - Serverless + TEE Compute :: Chutes SN8 - Prop firm Vanta Trading SN52 - AutoML :: Gradients SN62 - SWE agents :: Ridges AI | SN62 SN51 - Compute / GPU rental lium.io SN4 - TEE compute for enterprise :: Targon SN3 - 72B Distributed Training run :: templar SN41 - Prediction markets :: Almanac SN44 - Computer Vision Score - Subnet 44 SN68 - Drug discovery :: METANOVA SN18 - Weather Forecasting Zeus | SN 18 SN50 - Bitcoin prediction data :: Synthdata SN61 - Quantum computing :: qBitTensor Labs SN14 - Bitcoin mining pool :: TaoHash SN34 - Perp Dex :: 0xMarkets SN17 - 3D model generation :: 404 SN33 - Data analytics :: ReadyAI SN19 - [Since relaunched] RPC infrastructure :

Openτensor Foundaτion

1,172,641 просмотров • 3 месяцев назад