0G Major Update, the just-released DiLoCoX successfully completes training of a 10 billion parameter model in a low bandwidth environment for the first time
It's equivalent to completing the full training process of a large language model in a home network environment, directly breaking Web3 records and breaking centralized monopolies, which is of great significance
🧵Why is the DiLoCoX framework from 0G so important?
📌Allows Models to No Longer Be Limited by AllReduce
Traditional distributed training requires "all nodes to synchronize parameters in real-time" for every step, which places extremely high demands on the network. In low bandwidth environments, this synchronization becomes the biggest obstacle
DiLoCoX directly eliminates the "full synchronization at each step" mechanism, instead adopting a two-layer optimization strategy:
> Each node performs multiple steps of optimization locally, progressing independently
> Only during interval cycles does it participate in a global update using "pseudo gradients"
📌Hides Computational Waste Caused by Network Delays
Slow communication means that GPUs have to wait longer
To solve this problem, DiLoCoX introduces a very clever overlapping mechanism:
While calculating the current step, it preemptively sends the pseudo gradients for the next round
Thus, the waiting time for communication is "hidden" in the next calculation, and although each global update is slightly delayed, the training convergence is hardly affected
This delay mechanism saves 70% of GPU idle time, maximizing throughput
📌Compresses Communication Without Compromising Model Accuracy
Many prior low communication solutions have "over-compressed"—saving bandwidth but failing to achieve effective learning
DiLoCoX's approach is: compression should be timely and targeted
> It turns gradient compression into a dynamic decision-making process:
> During the initial exploration phase, compress more aggressively to enhance training efficiency
> In the middle to late convergence phase, gradually reduce the compression ratio to protect model accuracy
> Combining low-rank decomposition and quantization, it reduces communication volume to below 1%
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@0G_labs Research Team @spark_ren has released DiLoCoX, which is very important. Large models are the lifeblood of AI, and AI will be a battleground for competition in the coming years, from national levels to the advancement of web3 AI
In the past, large model training could almost only rely on centralized clusters. Web3 naturally adapts to the AI track, but the cost of large model training is simply too high
This has also led to Web3 AI innovations often being mocked as pseudo-innovations; to be honest, it's not surprising that outsiders find it laughable. Most projects just connect an API and set up an inference node, packaging it as a depin AI project
The emergence of DiLoCoX truly decentralizes the training authority to the decentralized network while also reducing costs and increasing efficiency
In the future, ordinary developers can participate in training, small teams can run 10 billion parameter models, and community-led AI architectures will no longer be just inference
From now on, Web3 AI will become possible, and the cracks in centralized model hegemony begin to appear from this moment @0G_Foundation @0x0g4i @michaelh_0g.
