Grayscale: Bittensor Trains 72B LLM on TAO Network

Bittensor$248.25 is back in the market's line of sight, and this time the pitch is not just token beta. Grayscale used a post on Tuesday to point investors toward what it framed as a genuine technical milestone: a 72 billion parameter large language model trained on Bittensor$248.25's permissionless network rather than in a centralised data centre. ^[1]

The asset manager's tweet linked to a new article by Low Beta on The Stack, arguing that Bittensor has now demonstrated decentralised model training at a scale that has largely remained the preserve of hyperscalers and well-funded AI labs. Grayscale paired that claim with the obvious market hook, noting that TAO has recently been one of the better-performing large-cap crypto assets. ^[2]

That pairing matters. Crypto has seen no shortage of AI-adjacent token narratives over the past two years, but most have traded more on story than on verifiable infrastructure progress. Grayscale's framing suggests Bittensor$248.25 is now trying to move the conversation from "AI token" speculation to whether decentralised machine learning networks can deliver work that was previously assumed to require tightly controlled, centralised compute environments.

The specific claim, a 72B parameter model trained across a permissionless network, is the substantive part. If it holds up under scrutiny, it would mark a meaningful proof point for decentralised compute coordination. Training large models is not simply a matter of aggregating spare hardware. It requires synchronisation, bandwidth, fault tolerance, incentive design, and a way to keep geographically distributed participants economically aligned while maintaining usable performance. That is exactly where many decentralised compute projects have struggled to move beyond theory. ^[2]

There was little substantive discussion in the immediate replies to Grayscale's post, and none added technical detail beyond the original claim. That, in itself, is telling. The market reaction around TAO has been louder than the public technical debate in the thread, which means much of the validation will likely need to come from deeper reporting, model benchmarks, and independent analysis rather than social media consensus. ^[1]

What matters next is whether Bittensor and outside researchers can show the model's performance, training methodology, and economic efficiency in enough detail to convince sceptics that this was more than a one-off demo. For now, Grayscale's tweet puts a sharper edge on the TAO bull case: decentralised AI is no longer being sold purely as ideology, but as infrastructure that may finally be able to do the job.