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@gregoriusthe3rd@TroyQuasar@TheTNetHunter@adaption_ai Thanks for the insight fren, a great analogy. My original comment was about complimentation. i couldnt see the wood for the trees but now understand alot more on how @QuasarModels will achieve this.They dont have to build on top (my confusion).
@TroyQuasar@TheTNetHunter@adaption_ai Just think I've found the part I was missing.
So a large model say Orion 100B could call Quasar via tool calling for long context?
@TroyQuasar@TheTNetHunter@adaption_ai But my point is what you are building could compliment models built on Bittensor by giving said models (hopefully soon) SOTA context. If I'm wrong,lesson learned I will do more research.
Orion-100B was made possible by a series of advances:
- The creation and utilization of ResBM, currently the state-of-the-art (SOTA) technique for lossless activation compression in LLM training,
-A custom, fault-tolerant peer-to-peer network protocol that optimizes throughput & latency across heterogeneous GPU nodes.
-Reliable distributed variable synchronization.
These advancements have enabled IOTA to increase its MFU capabilities by an order of magnitude when compared to previous results, and support training across many pipeline stages without significant costs to net throughput.
Orion-100B from @IOTA_SN9 just shattered the ceiling on decentralized AI training
A full 100-billion-parameter model trained across 48 single A100 GPUs in 5 different US datacenters, coordinated entirely over the open internet via Bittensor's Subnet 9. The team achieved 30% model FLOP utilization β roughly 65% of datacenter training speed β while using distributed hardware that costs a fraction as much, thanks to their breakthrough ResBM compression that shrinks activation data by 64Γ. A single contributor with one GPU can now participate in frontier-scale training, and this system scaled 67Γ in model size in just one month. The era of truly permissionless AI is no longer theoretical β it's training a 100B model right now.
Today, we are launching the first stage of Project Orion.
Our early pre-training run of Orion-100B achieves upward of 65% of data-center training efficiency on hardware costing a fraction of the price.
Orion-100B is the first proof point for a simple idea: that underutilized compute around the world can be turned into frontier training capacity.
We believe that this work presents, for the first time, an economically compelling case for training large models using distributed approaches.
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Bittensor >> Conviction
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Well played, @subnetradarcom.
Conviction is now live.
Iβm looking forward to seeing real builders join the still very short list of owners willing to lock their own alpha.
Because if an owner has no conviction in their own project, and no conviction in Bittensor itself, then risking precious TAO alongside them makes little sense.
Iβll give every owner a few days to lock their alpha and demonstrate where they truly stand.
If they choose not to, I will consider my positions at risk.
At that point, it may be time to sell and join the club of serious owners instead.
Conviction is more than a mechanism.
Itβs a filter.