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A crypto project actually trained a 72B parameter AI model from scratch using decentralized GPU compute. Not fine-tuned, not a wrapper: trained from zero. The model benchmarks competitively against Meta's LLaMA 3 on reasoning tasks, and the entire training run cost a fraction of what centralized labs spend. If... show more

VirtualBacon

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40,543 次观看 • 2 个月前 •via X (Twitter)

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We're releasing Paris 2.0, which, to our knowledge, is the world's first decentralized trained video generation model. We benchmarked it against a monolithic model trained on the same data and compute budget, and Paris 2.0 outperformed the monolithic by ~2x on FVD benchmark.

We're releasing Paris 2.0, which, to our knowledge, is the world's first decentralized trained video generation model. We benchmarked it against a monolithic model trained on the same data and compute budget, and Paris 2.0 outperformed the monolithic by ~2x on FVD benchmark.

bidhan

447,268 次观看 • 13 天前

Sat down with Bittensor co-founder Jacob Steeves (Const) for a deep dive into the state of Bittensor in 2026. We covered how Subnet 3 trained a 72B parameter model in a fully permissionless decentralized way, how subnets create markets for digital commodities like gradients and inference, the dynamic TAO tokenomics that lock emissions into subnet liquidity pools, and why open ownership of AI matters more than ever as centralized labs head toward trillion-dollar IPOs. 0:00 Intro: Bittensor Co-Founder Jacob Steeves 1:13 Jacob's Background, From Deep Learning to Crypto 4:45 Founding Bittensor: The Monetary Computer 7:58 How Subnets Work as Markets for Digital Commodities 11:18 Subnet 3: Decentralized Model Training 15:28 Permissionless Training at Internet Scale 18:52 Why This Matters: Open Source AI Ownership 25:53 Subnet Tour: Optimization, Inference, and Compute 29:55 Affine: Beating Qwen 30B via Market Incentives 34:26 TAO Tokenomics and Dynamic TAO 51:54 How Investors Participate: Just Buy TAO 54:05 Open Ownership vs Fiat AI

Sat down with Bittensor co-founder Jacob Steeves (Const) for a deep dive into the state of Bittensor in 2026. We covered how Subnet 3 trained a 72B parameter model in a fully permissionless decentralized way, how subnets create markets for digital commodities like gradients and inference, the dynamic TAO tokenomics that lock emissions into subnet liquidity pools, and why open ownership of AI matters more than ever as centralized labs head toward trillion-dollar IPOs. 0:00 Intro: Bittensor Co-Founder Jacob Steeves 1:13 Jacob's Background, From Deep Learning to Crypto 4:45 Founding Bittensor: The Monetary Computer 7:58 How Subnets Work as Markets for Digital Commodities 11:18 Subnet 3: Decentralized Model Training 15:28 Permissionless Training at Internet Scale 18:52 Why This Matters: Open Source AI Ownership 25:53 Subnet Tour: Optimization, Inference, and Compute 29:55 Affine: Beating Qwen 30B via Market Incentives 34:26 TAO Tokenomics and Dynamic TAO 51:54 How Investors Participate: Just Buy TAO 54:05 Open Ownership vs Fiat AI

VirtualBacon

41,859 次观看 • 2 个月前

On the The All-In Podcast this week, Chamath Palihapitiya asked NVIDIA CEO Jensen Huang about decentralized AI training, calling our Covenant-72B run "a pretty crazy technical accomplishment." One correction: it's 72 billion parameters, not four. Trained permissionlessly across 70+ contributors on commodity internet. The largest model ever pre-trained on fully decentralized infrastructure. Jensen's answer is worth hearing too.

On the The All-In Podcast this week, Chamath Palihapitiya asked NVIDIA CEO Jensen Huang about decentralized AI training, calling our Covenant-72B run "a pretty crazy technical accomplishment." One correction: it's 72 billion parameters, not four. Trained permissionlessly across 70+ contributors on commodity internet. The largest model ever pre-trained on fully decentralized infrastructure. Jensen's answer is worth hearing too.

templar

470,959 次观看 • 2 个月前

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.

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 次观看 • 1 年前

Today we’re launching INTELLECT-2: The first decentralized 32B-parameter RL training run open to join for anyone with compute — fully permissionless. Scaling towards frontier reasoning across coding, math and science.

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Prime Intellect

353,394 次观看 • 1 年前

This Paper from Google DeepMind is a landmark one. 📚 "Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters" It may have contributed to the 01 Model from OpenAI or the principle may have been long known to OpenAI. The paper basically says - Searching at inference will give you great final result from the LLM. Now from Strawberry model is making use of inference time compute strategies using Search techniques over the response space to improve reasoning. ------ The Podcast is generated with Google's illuminate, the tool trained on AI & science-related Arxiv papers.

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Rohan Paul

48,980 次观看 • 1 年前

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

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Investor Jordan 🌪️

55,544 次观看 • 11 个月前

Jensen: if your AI is better than the attacker's AI, you're fine. A small malicious team is never going to have more compute than OpenAI/Anthropic and certainly not more than a country. Hope Jensen is right 🤞

Jensen: if your AI is better than the attacker's AI, you're fine. A small malicious team is never going to have more compute than OpenAI/Anthropic and certainly not more than a country. Hope Jensen is right 🤞

Matthew Berman

51,493 次观看 • 28 天前

train YOLOv9 on your dataset tutorial - run inference with a pre-trained COCO model - fine-tune model on custom dataset - evaluate the trained model - run inference with a fine-tuned model blogpost: ↓ read more

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SkalskiP

111,792 次观看 • 2 年前

⚡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.

⚡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

43,559 次观看 • 9 个月前

Nosana, a $378M FDV DePIN project, pivoted from code deployment to AI compute, building a GPU network without overvaluation. Launching its mainnet Jan 14, Nosana is a rare Solana-native gem, poised to compete with giants like Render and in the AI compute space and is definitely one to watch!

Nosana, a $378M FDV DePIN project, pivoted from code deployment to AI compute, building a GPU network without overvaluation. Launching its mainnet Jan 14, Nosana is a rare Solana-native gem, poised to compete with giants like Render and in the AI compute space and is definitely one to watch!

VirtualBacon

56,387 次观看 • 1 年前

Dylan on why Anthropic leading on models doesn't mean OpenAI loses. Even the tier 2 and 3 labs will sell out of tokens: "Everyone's like Anthropic won. They had Mythos, Opus 4.7 and their revenue is adding $10 billion a month all before OpenAI's alleged Spud release. So clearly Anthropic is in the lead and OpenAI is cooked. Anthropic has such bounds on compute, they can only grow it so fast. Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. Now Anthropic is like I wish we had a lot more compute. OpenAI just raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying. By the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon. Anthropic may get to charge 70%+ gross margins but if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. There is such demand for these tokens and such limitations on compute. It's pretty clear even the tier 2 or tier 3 lab are going to be sold out of tokens. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure."

Dylan on why Anthropic leading on models doesn't mean OpenAI loses. Even the tier 2 and 3 labs will sell out of tokens: "Everyone's like Anthropic won. They had Mythos, Opus 4.7 and their revenue is adding $10 billion a month all before OpenAI's alleged Spud release. So clearly Anthropic is in the lead and OpenAI is cooked. Anthropic has such bounds on compute, they can only grow it so fast. Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. Now Anthropic is like I wish we had a lot more compute. OpenAI just raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying. By the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon. Anthropic may get to charge 70%+ gross margins but if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. There is such demand for these tokens and such limitations on compute. It's pretty clear even the tier 2 or tier 3 lab are going to be sold out of tokens. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure."

Patrick OShaughnessy

294,850 次观看 • 1 个月前

"It all comes down to compute." Dmitri Alperovitch from Silverado Policy Accelerator explains: "The single most important input to winning is compute the processing power used to train and run AI models. Let me say that plainly, because it is defining that everything that congress and this committee do on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."

"It all comes down to compute." Dmitri Alperovitch from Silverado Policy Accelerator explains: "The single most important input to winning is compute the processing power used to train and run AI models. Let me say that plainly, because it is defining that everything that congress and this committee do on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."

Select Committee on China

41,507 次观看 • 1 个月前

Elon Musk: In the future, every country will have its own AI clusters. “It's currently very difficult to actually build an AI cluster and have it run. It's not like just pulling a computer out of a box. They are currently very difficult to run. And are you going to be training a frontier model? Because if you're training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But, over time, I think every country will have AI compute clusters. It's just going to be a normal thing.” Future Investment Initiative, October 29, 2024

Elon Musk: In the future, every country will have its own AI clusters. “It's currently very difficult to actually build an AI cluster and have it run. It's not like just pulling a computer out of a box. They are currently very difficult to run. And are you going to be training a frontier model? Because if you're training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But, over time, I think every country will have AI compute clusters. It's just going to be a normal thing.” Future Investment Initiative, October 29, 2024

ELON CLIPS

95,313 次观看 • 6 个月前

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 次观看 • 11 个月前

Covenant Labs just did a 90-minute AMA breaking down their 3 Bittensor subnets. templar. basilica. grail. Pre-training, compute, and post-training under one roof. Most people missed it. Here's everything they said. Covenant is building what they call the "end to end intelligence continuum." Three subnets. Three layers of the AI stack. All permissionless. Templar (SN3) handles decentralized pre-training. Basilica (SN39) handles compute. Grail (SN81) handles RL post-training. Distributed State, the lead, put it bluntly. Decentralized training is "humanity's last dance." Not about beating OpenAI head to head. About creating optionality. About making it cheap enough for anyone to train models. The gap between academia and frontier labs is growing exponentially. Researchers can't afford to experiment. The actual training run costs 5% of the reported budget. The other 95% is experimentation. If Covenant cracks cheap training, that entire surface area opens up. On Templar specifically: • Hit 39% emission on Bittensor. Highest since Apex was the only subnet on the network • Covenant-72B trained permissionlessly with 70+ contributors on commodity internet • 1.1 trillion tokens processed. No centralized data center • Performance competitive with LLaMA-2-70B On Grail, something flew under the radar. They built Pulse. A weight synchronization method that compresses model updates by 100x. • In RL post-training, only ~1% of weights update per step • Pulse exploits that sparsity. Lossless compression • Prime Intellect's comparable system took 14 minutes to sync a 30B model • Pulse makes decentralized RL training actually feasible at scale • Already used by Cursor The lead researcher on Grail said they've trained on math, code, and GPU kernels. Got 40-60% improvement on benchmarks. Working toward agentic training with 100K+ token context and 30B+ parameter models. On Basilica, the compute subnet: The team was blunt. Just reselling GPU hours is a 5-10% margin game. Traditional compute providers already do that. Their play is value-added services. • "GPU as code." No dashboard. No UI. Agents interact via SDK • Custom scheduler that places workloads across heterogeneous hardware • Verification checks for GPU, CPU, bandwidth, memory, storage, and OS security • Partnerships with providers like Mass Compute for 10-20% below market pricing • Miners compete on useful infrastructure, not just GPU hours Sam then went on a rant about the miner burn debate. His take: Bittensor had to grow up. dTAO introduced investors. The old "miners are God" philosophy doesn't hold. • Subnet owners have a duty to protect token value • Miners are a resource optimization exercise, not a cost reduction exercise • 100% miner emissions on compute subnets = immediate sell pressure • The 41% miner allocation is arbitrary. Different business models need different splits • Fish (who started burns) agreed. Burns usually mean the validation isn't mature enough The bigger point. You can't police burns. Subnets just send to their own keys instead of the burn address. Subnet 28 does exactly that. Sam's position: judge subnets on outcomes, not process. Const has changed the protocol 9-10 times in 2 years. That iteration speed is Bittensor's actual moat. The whole Covenant thesis is playing out in real time. TAO is up 100%+ in a month. Jensen Huang name-dropped the network. Grayscale has an ETF filing. But the real story is three subnets quietly building every layer of decentralized AI.

Covenant Labs just did a 90-minute AMA breaking down their 3 Bittensor subnets. templar. basilica. grail. Pre-training, compute, and post-training under one roof. Most people missed it. Here's everything they said. Covenant is building what they call the "end to end intelligence continuum." Three subnets. Three layers of the AI stack. All permissionless. Templar (SN3) handles decentralized pre-training. Basilica (SN39) handles compute. Grail (SN81) handles RL post-training. Distributed State, the lead, put it bluntly. Decentralized training is "humanity's last dance." Not about beating OpenAI head to head. About creating optionality. About making it cheap enough for anyone to train models. The gap between academia and frontier labs is growing exponentially. Researchers can't afford to experiment. The actual training run costs 5% of the reported budget. The other 95% is experimentation. If Covenant cracks cheap training, that entire surface area opens up. On Templar specifically: • Hit 39% emission on Bittensor. Highest since Apex was the only subnet on the network • Covenant-72B trained permissionlessly with 70+ contributors on commodity internet • 1.1 trillion tokens processed. No centralized data center • Performance competitive with LLaMA-2-70B On Grail, something flew under the radar. They built Pulse. A weight synchronization method that compresses model updates by 100x. • In RL post-training, only ~1% of weights update per step • Pulse exploits that sparsity. Lossless compression • Prime Intellect's comparable system took 14 minutes to sync a 30B model • Pulse makes decentralized RL training actually feasible at scale • Already used by Cursor The lead researcher on Grail said they've trained on math, code, and GPU kernels. Got 40-60% improvement on benchmarks. Working toward agentic training with 100K+ token context and 30B+ parameter models. On Basilica, the compute subnet: The team was blunt. Just reselling GPU hours is a 5-10% margin game. Traditional compute providers already do that. Their play is value-added services. • "GPU as code." No dashboard. No UI. Agents interact via SDK • Custom scheduler that places workloads across heterogeneous hardware • Verification checks for GPU, CPU, bandwidth, memory, storage, and OS security • Partnerships with providers like Mass Compute for 10-20% below market pricing • Miners compete on useful infrastructure, not just GPU hours Sam then went on a rant about the miner burn debate. His take: Bittensor had to grow up. dTAO introduced investors. The old "miners are God" philosophy doesn't hold. • Subnet owners have a duty to protect token value • Miners are a resource optimization exercise, not a cost reduction exercise • 100% miner emissions on compute subnets = immediate sell pressure • The 41% miner allocation is arbitrary. Different business models need different splits • Fish (who started burns) agreed. Burns usually mean the validation isn't mature enough The bigger point. You can't police burns. Subnets just send to their own keys instead of the burn address. Subnet 28 does exactly that. Sam's position: judge subnets on outcomes, not process. Const has changed the protocol 9-10 times in 2 years. That iteration speed is Bittensor's actual moat. The whole Covenant thesis is playing out in real time. TAO is up 100%+ in a month. Jensen Huang name-dropped the network. Grayscale has an ETF filing. But the real story is three subnets quietly building every layer of decentralized AI.

Jesus Martinez

26,522 次观看 • 2 个月前

🚨 xAI: GROK’S REASONING POWER HAS GOTTEN A MASSIVE COMPUTE BOOST “It’s important to realize there are two types of training compute. One is the pre-training compute—that’s from Grok 2 to Grok 3. But from Grok 3 to Grok 4, we’re actually putting a lot of compute into reasoning.” Source: xAI Elon Musk

🚨 xAI: GROK’S REASONING POWER HAS GOTTEN A MASSIVE COMPUTE BOOST “It’s important to realize there are two types of training compute. One is the pre-training compute—that’s from Grok 2 to Grok 3. But from Grok 3 to Grok 4, we’re actually putting a lot of compute into reasoning.” Source: xAI Elon Musk

Mario Nawfal

49,508 次观看 • 11 个月前

We’re thrilled to announce that Gata Decentralized GPU Cloud is now LIVE! Start today → Built to accelerate the decentralization of the open AI economy, Gata enables anyone, anywhere to access decentralized GPU infrastructure, to build, train, and deploy AI models with full transparency and efficiency. GPU credits are now available for purchase directly with $GATA, creating a seamless bridge between compute and the decentralized economy.

We’re thrilled to announce that Gata Decentralized GPU Cloud is now LIVE! Start today → Built to accelerate the decentralization of the open AI economy, Gata enables anyone, anywhere to access decentralized GPU infrastructure, to build, train, and deploy AI models with full transparency and efficiency. GPU credits are now available for purchase directly with $GATA, creating a seamless bridge between compute and the decentralized economy.

Gata

47,612 次观看 • 7 个月前

Introducing Pond - a decentralized AI model layer with crypto-native AI models trained on on-chain data. Pond is excited to announce a $7.5M Seed round, led by Archetype with , Delphi Ventures, Coinbase Ventures 🛡️, ANAGRAM, and more.

Introducing Pond - a decentralized AI model layer with crypto-native AI models trained on on-chain data. Pond is excited to announce a $7.5M Seed round, led by Archetype with , Delphi Ventures, Coinbase Ventures 🛡️, ANAGRAM, and more.

Pond

242,629 次观看 • 1 年前