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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 views • 6 months ago •via X (Twitter)

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16 Comments

Beck ττ6 months ago

$TAO will be a trillions marketcap

Mark //6 months ago

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

Crypτnomad6 months ago

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.

Bart Barton6 months ago

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

Lazz.eth6 months ago

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

Dmitry6 months ago

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

Hellas6 months ago

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

Vlad Ventura6 months ago

👀

0xChem6 months ago

Wait till the find the other subnets

Sebastien Ng6 months ago

@opentensor $TAO pulled yet?

Gata6 months ago

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.

Aliyu Zubairu Gunigi6 months ago

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

Chain Alpha6 months ago

finally, something actually built, not just hyped WAGMI

YH Phua6 months ago

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. 😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡😡

CVAlpha6 months ago

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

Robert Nass6 months ago

Decentralized training just proved serious viability

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111,876 views • 2 years ago

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19,009 views • 4 months ago

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295,449 views • 6 months ago

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VirtualBacon

57,411 views • 1 year ago

$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 views • 3 months ago