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Masao Fast News
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If you pay attention, you'll notice that every day hundreds of new AI projects pop up.
New models.
New AI agents.
New AI applications.
But there's a question that few people mention:
Who will provide the computing infrastructure for all those systems?
That's when I started looking into HACA (Hybrid AI Compute Architecture) from @OpenGradient .
Bringing AI onto the blockchain sounds enticing, but the reality is anything but simple.
AI needs powerful GPUs, massive computational workloads, and near real-time processing capabilities.
While blockchain was not designed for such tasks.
Instead of trying to shove the entire AI on-chain, OpenGradient took a more pragmatic approach.
They split the network into various types of nodes with specific tasks.
Inference Nodes handle AI inference.
Full Nodes take care of verification, consensus, and payment.
Data Nodes securely gather data from external sources.
Each component focuses on what it does best.
Thanks to this, the system can scale without completely sacrificing transparency.
What I find fascinating is that OpenGradient doesn't view the blockchain as the place to perform all computations.
The blockchain serves as a verification and coordination layer.
While the AI compute is handled by specialized infrastructure behind the scenes.
The project also integrates TEE and zkML to enhance the verification of inference results instead of relying solely on trust.
In my opinion, HACA is one of the most core technologies of OpenGradient.
Because if you want to build a truly decentralized AI network, the challenge isn't just about having many GPUs.
It's also about how to scale AI while ensuring it can be verified transparently.
@OpenGradient
$OPG #OPG
New models.
New AI agents.
New AI applications.
But there's a question that few people mention:
Who will provide the computing infrastructure for all those systems?
That's when I started looking into HACA (Hybrid AI Compute Architecture) from @OpenGradient .
Bringing AI onto the blockchain sounds enticing, but the reality is anything but simple.
AI needs powerful GPUs, massive computational workloads, and near real-time processing capabilities.
While blockchain was not designed for such tasks.
Instead of trying to shove the entire AI on-chain, OpenGradient took a more pragmatic approach.
They split the network into various types of nodes with specific tasks.
Inference Nodes handle AI inference.
Full Nodes take care of verification, consensus, and payment.
Data Nodes securely gather data from external sources.
Each component focuses on what it does best.
Thanks to this, the system can scale without completely sacrificing transparency.
What I find fascinating is that OpenGradient doesn't view the blockchain as the place to perform all computations.
The blockchain serves as a verification and coordination layer.
While the AI compute is handled by specialized infrastructure behind the scenes.
The project also integrates TEE and zkML to enhance the verification of inference results instead of relying solely on trust.
In my opinion, HACA is one of the most core technologies of OpenGradient.
Because if you want to build a truly decentralized AI network, the challenge isn't just about having many GPUs.
It's also about how to scale AI while ensuring it can be verified transparently.
@OpenGradient
$OPG #OPG
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