#opg $OPG @OpenGradient Why force every single validator to rerun the full AI model when one solid run plus a proof should be enough? That always struck me as the core flaw in how most chains tried to handle inference. It turns a one time computation into network wide busywork that nobody needs OpenGradient went with validation computation separation instead. Inference nodes handle running the model and pass back the result with proof attached Validator nodes on the OPG chain skip the model work completely.#OPG
This split in OpenGradient keeps the heavy computation off the validation layer. The validators only verify the proof rather than re executing the inference across the full set of nodes. It stops the wasteful duplication that kills performance Without separation you end up with every validator acting like it has to be a full inference machine. That does not scale and turns calls into slow processes. OpenGradient's approach lets inference nodes do what they are built for while validators stay efficient at checking evidence.@OpenGradient
OpenGradient made the separation practical because it matches how real compute works. Not every node type needs to repeat the expensive step and is still depends on the proof being reliable enough without re running the work but the separation removes a clear limiting the rate of output Focus on whether the design actually avoids forcing all validators to replicate inference or if it just claims separation without changing how the nodes operate.@OpenGradient
$OPG
This split in OpenGradient keeps the heavy computation off the validation layer. The validators only verify the proof rather than re executing the inference across the full set of nodes. It stops the wasteful duplication that kills performance Without separation you end up with every validator acting like it has to be a full inference machine. That does not scale and turns calls into slow processes. OpenGradient's approach lets inference nodes do what they are built for while validators stay efficient at checking evidence.@OpenGradient
OpenGradient made the separation practical because it matches how real compute works. Not every node type needs to repeat the expensive step and is still depends on the proof being reliable enough without re running the work but the separation removes a clear limiting the rate of output Focus on whether the design actually avoids forcing all validators to replicate inference or if it just claims separation without changing how the nodes operate.@OpenGradient
$OPG