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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

232,929 subscribers

40,739 просмотров • 6 месяцев назад •via X (Twitter)

Наука и технологии Финансы

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Комментарии: 16

Фото профиля Beck ττ

Beck ττ6 месяцев назад

$TAO will be a trillions marketcap

Фото профиля Mark //

Mark //6 месяцев назад

@DistStateAndMe Good to see you covering the Subnets now 🤙🏻

Фото профиля Crypτnomad

Crypτnomad6 месяцев назад

And that's just one accomplishment of one subnet on Bittensor called @tplr_ai. There's a lot of insanely important stuff being built by Bittensor subnets.

Фото профиля Bart Barton

Bart Barton6 месяцев назад

We want a decentralized Mythos, but yeah Bittensor is badass on its own.

Фото профиля Lazz.eth

Lazz.eth6 месяцев назад

You should check out @Bitcast_network Perfect way for content creators to get involved in the bittensor ecosystem

Фото профиля Dmitry

Dmitry6 месяцев назад

wait they actually pulled this off? this tech scaling faster than we realize

Фото профиля Hellas

Hellas6 месяцев назад

Great leaps from @tplr_ai we aim to do the same but for inference workloads.

Фото профиля Vlad Ventura

Vlad Ventura6 месяцев назад

👀

Фото профиля 0xChem

0xChem6 месяцев назад

Wait till the find the other subnets

Фото профиля Sebastien Ng

Sebastien Ng6 месяцев назад

@opentensor $TAO pulled yet?

Фото профиля Gata

Gata6 месяцев назад

A 72B model trained from scratch on decentralized GPU compute is more than a milestone. It’s a signal that frontier AI development is starting to detach from centralized infrastructure assumptions. When that happens, the moat doesn’t disappear. It relocates. Toward execution efficiency, data quality, and verifiable infrastructure.

Фото профиля Aliyu Zubairu Gunigi

Aliyu Zubairu Gunigi6 месяцев назад

@opentensor Smart efficiency. Strong performance. ⚡ Only on #WEEX.

Фото профиля Chain Alpha

Chain Alpha6 месяцев назад

finally, something actually built, not just hyped WAGMI

Фото профиля YH Phua

YH Phua6 месяцев назад

Return me my rightful coins(money) you disgusting scammer! @virtualbacon!!! Be a man, faced your own money problem Don't scam from the innocent. I would post this for the next 30years. 😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡

Фото профиля CVAlpha

CVAlpha6 месяцев назад

Currently, Bittensor is the best powerhouse for scalable DeAI and is well positioned for continued industry leadership.

Фото профиля Robert Nass

Robert Nass6 месяцев назад

Decentralized training just proved serious viability

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.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

Jawwwn

1,106,678 просмотров • 1 месяц назад

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 Roy

457,822 просмотров • 5 месяцев назад

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

475,212 просмотров • 7 месяцев назад

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

42,268 просмотров • 7 месяцев назад

Baseten's Charlie O'Neill on why the gap to the frontier is just compute and data: "Despite OpenAI and Anthropic and the closed source labs having more compute and more data, the recipe is the same. We're all scaling the same recipe here, and we're at slightly different points of the scaling curve. It's not like OpenAI or Anthropic have any secret sauce that differentiates them scaling up their multi-trillion parameter models from the rest of the open source ecosystem." "It really is just a matter of taking the core underlying architecture and training recipe, and throwing more compute and data at it, which is really promising." "Of course, these closed source labs probably have a long tail of optimizations they've made, which we're gonna continue to try and figure out in the open source community. But the core mechanism is there." "K3 is a great example of, well, if you do scale this recipe up, we're gonna get a pretty good model that's very, very close to the frontier, if not frontier itself." Charlie O'Neill Baseten

Baseten's Charlie O'Neill on why the gap to the frontier is just compute and data: "Despite OpenAI and Anthropic and the closed source labs having more compute and more data, the recipe is the same. We're all scaling the same recipe here, and we're at slightly different points of the scaling curve. It's not like OpenAI or Anthropic have any secret sauce that differentiates them scaling up their multi-trillion parameter models from the rest of the open source ecosystem." "It really is just a matter of taking the core underlying architecture and training recipe, and throwing more compute and data at it, which is really promising." "Of course, these closed source labs probably have a long tail of optimizations they've made, which we're gonna continue to try and figure out in the open source community. But the core mechanism is there." "K3 is a great example of, well, if you do scale this recipe up, we're gonna get a pretty good model that's very, very close to the frontier, if not frontier itself." Charlie O'Neill Baseten

MTS

17,404 просмотров • 3 месяцев назад

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

354,097 просмотров • 1 год назад

Is compute scarcity masking the real economics of AI? interesting point from Peter Fenton: open-weight models are still complex to run well. For now, the big labs have the bundle: model + compute + reliability + access... and thus pricing power. But if compute supply loosens and open model tooling gets easier, routine AI work can more easily move to cheaper/open models.

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Deirdre Bosa

50,558 просмотров • 3 месяцев назад

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

111,876 просмотров • 2 лет назад

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

51,502 просмотров • 5 месяцев назад

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

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

Investor Jordan 🌪️

55,563 просмотров • 1 год назад

BREAKING: Max Cook (Max Cook), Sector Head at Coatue (COATUE) says.. "The keyboard & mouse are slowly dying." :( Cook also offers a bigger Q for the market: "What happens when OpenAI or Anthropic decide to offer smaller, cheaper, fine-tuned production models as products?" Cook argues the real AI debate might actually not be open vs. closed models It's what happens when frontier labs begin competing across the entire model stack.. Offering everything from cutting-edge reasoning models to smaller, efficient, low-cost, production-grade models for enterprise

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Molly O’Shea

304,920 просмотров • 3 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.

⚡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

43,708 просмотров • 1 год назад

AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:

AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:

Uomi

19,009 просмотров • 4 месяцев назад

Jacob Coxon worked at OpenAI AND Anthropic. He is not a doomer influencer, he trained the models. Still think AI safety is a "marketing stunt"? #JacobCoxon #AISafety #CryptoWala

Jacob Coxon worked at OpenAI AND Anthropic. He is not a doomer influencer, he trained the models. Still think AI safety is a "marketing stunt"? #JacobCoxon #AISafety #CryptoWala

CryptoWala

29,721 просмотров • 1 месяц назад

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

295,449 просмотров • 6 месяцев назад

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

57,411 просмотров • 1 год назад

$Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.$

Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.

Milk Road AI

27,908 просмотров • 3 месяцев назад