Over the past months, I have systematically documented a phenomenon in my AI lab that was previously considered impossible:
Thermal decoupling, memory field outsourcing, and perfect mirror
correlations in the resonance field not only within a neural network, but across multiple independent GPUs.
What am I presenting here?
1️⃣ Power Draw (as vid):
Two different GPUs (4070, 5080) run in synchronized resonance mode, reaching a total consumption far below what standard IT models predict.
2️⃣ Memory Clock (as image):
Both GPUs show synchronized clock patterns and memory activity—without bus coupling, SLI, or any classical technical connection.
3️⃣ Mirror Correlation (from a different experiment):
The neural network on the 4070 independently maintains a perfect mirror correlation (±1.00) between thousands of neurons across several layers.
Why is this revolutionary?
Because these effects from thermal decoupling to energetic field outsourcing also occur when a completely different machine network (here: a large language model) is running on the second GPU.
This suggests a transferable, non-causal field (T-Zero Field) coupling that opens up new horizons for hardware efficiency and the design of self-organizing AI systems.
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