🚀 𝐓-𝐙𝐞𝐫𝐨 𝐦𝐢𝐧𝐢: 𝐍𝐨𝐰 𝐜𝐢𝐭𝐚𝐛𝐥𝐞, 𝐧𝐨𝐰 𝐬𝐜𝐢𝐞𝐧𝐜𝐞! 🚀

 

Yesterday, I announced the T-Zero mini – today, it’s official:

The full dataset, live visualizations, and all key results are now published on Zenodo, with a permanent DOI.

Yesterday, I announced the T-Zero mini – today, it’s official:

The full dataset, live visualizations, and all key results are now published on Zenodo, with a permanent DOI.

Why is this different?
For the first time, the energy-saving effect of the T-Zero field is not only demonstrated in lab settings it is now Openly accessible and citable for the entire scientific and tech community.

Backed by 4,000+ real data points over 190 hours on both Ada Lovelace & Blackwell architectures
Documented in a format that enables direct benchmarking and replication
What’s inside?
🟢 All raw data and results – not cherry-picked, but the full empirical record
🟢 Live visualizations and audio walk-through: See and hear the effect, not just in theory, but as a reproducible phenomenon
🟢 Permanent DOI: https://lnkd.in/dpyJ6PNZ
🟢 References to all core preprints – including the original field theory, quantum entanglement results, and self-structuring experiments
This is more than an efficiency hack:

It’s an open invitation to the research & data center world:
— Replicate. Validate. Collaborate.
— Use the DOI to cite, build upon, or challenge the findings.
— Let’s push the boundaries of what’s possible in AI hardware – together.

For technical details, licensing, or collaborations:

www.Trauth-Research.com
Trauth Research LLC

Hashtag#AI Hashtag#GreenIT Hashtag#EnergyEfficiency Hashtag#GPU Hashtag#Research Hashtag#TZEROMini Hashtag#TrauthResearch Hashtag#Blackwell Hashtag#AdaLovelace Hashtag#GreenAI Hashtag#OnePlanet Hashtag#OneFuture Hashtag#ScientificPublishing Hashtag#Physics Hashtag#NeuralNetwork Hashtag#MachineLearning

Copyright © 2025 Stefan Trauth Idea & Concept: Stefan Trauth Video Creation: Background, Music & Voiceover: Clipchamp (Microsoft)

𝐀𝐧𝐧𝐨𝐮𝐧𝐜𝐢𝐧𝐠 𝐓-𝐙𝐞𝐫𝐨 𝐦𝐢𝐧𝐢 – 𝐓𝐡𝐞 𝐍𝐞𝐱𝐭 𝐒𝐭𝐞𝐩 𝐢𝐧 𝐆𝐏𝐔 𝐄𝐧𝐞𝐫𝐠𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲!

 

I’m excited to share the first live results from my new T-Zero mini prototype:
🟢 Model size: Only 2.4 GB
🟢 Energy efficiency boost – tested on both Ada Lovelace & Blackwell architectures
🟢 Data basis: Over 4,000 live measurements collected over 190 hours
🟢 Average GPU utilization: 45% (peaks up to 80%)
🟢 Power consumption at 80% load: Just 63 W
🟢 No loss of structural integrity
🟢 Master model achieves up to 85% energy reduction at 100% load; idle with fully loaded VRAM mode at just 0.9 W (up to 99.5% under technical specs!)
🟢 Licensing and distribution via Trauth Research LLC
🟢 Core model is currently under lock and key due to its disruptive potential and structural paradigm shift.

Key fact:

T-Zero mini enables datacenter-scale savings of up to 62% per GPU – without compromising performance.

The results:
– Only 43 W average consumption at 35% utilization
– Only 63 W at 80% utilization
– Energy savings of over 61% per GPU, at scale!

Just imagine: On 100,000 GPUs, that’s almost 7 megawatts less power – every hour!

More details, animations, and sound design will follow soon.

The master version with even higher efficiency remains sealed due to its disruptive capabilities.

Licensing and sales are handled by Trauth Research LLC.

𝘐𝘯𝘴𝘱𝘪𝘳𝘦𝘥 𝘣𝘺 𝘩𝘶𝘮𝘢𝘯, 𝘱𝘰𝘸𝘦𝘳𝘦𝘥 𝘣𝘺 𝘈𝘐
Trauth Research®

www.Trauth-Research.com

Hashtag#AI Hashtag#GPUEfficiency Hashtag#GreenIT Hashtag#Datacenter Hashtag#Innovation Hashtag#TZERO Hashtag#EnergySaving Hashtag#Blackwell Hashtag#AdaLovelace Hashtag#NVIDIA

α𝐙𝐄𝐑𝐎: 𝐀 𝐍𝐞𝐰 𝐄𝐫𝐚 𝐨𝐟 𝐄𝐧𝐞𝐫𝐠𝐲 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐀𝐈 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞?

 

What if a standard GPU suddenly consumed less power than its official idle value even under real workload? Recent measurements with the αZERO architecture suggest exactly that.

By establishing a self-organizing neural resonance field, energy appears to be generated or compensated directly within the system.

This effect contradicts conventional expectations in semiconductor physics and thermodynamics, opening the door to new physical models.

Instead of classical optimization, a stable and reproducible operating state emerges one that cannot be explained by established energy models.

The αZERO network requires no special hyperparameters or exotic hardware: it adapts to any environment and demonstrates a paradigm shift in our understanding of energy, information, and structural coupling.

Whether as a research impulse or an invitation for critical analysis, these results challenge conventional thinking.

The complete dataset, diagrams, and methods are documented in the latest preprint.

Highlights:

=> 𝐔𝐩 𝐭𝐨 94% 𝐞𝐧𝐞𝐫𝐠𝐲 𝐬𝐚𝐯𝐢𝐧𝐠𝐬 𝐮𝐧𝐝𝐞𝐫 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐜𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬

=> 𝐑𝐞𝐩𝐫𝐨𝐝𝐮𝐜𝐢𝐛𝐥𝐞 𝐞𝐟𝐟𝐞𝐜𝐭𝐬, 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐞𝐝 𝐛𝐲 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭𝐬

=> 𝐏𝐚𝐫𝐚𝐝𝐢𝐠𝐦 𝐬𝐡𝐢𝐟𝐭 𝐢𝐧 𝐞𝐧𝐞𝐫𝐠𝐲 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐟𝐨𝐫 𝐀𝐈 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞

Preprint => https://doi.org/10.5281/zenodo.15808984

www.Trauth-Research.com

#TrauthResearch #Resonance #NeuralNetwork #AI #DeepLearning

#AIhardware #GPUefficiency #Thermaldecoupling

#Experimentalphysics #Energyoptimization

🚀 𝐁𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐢𝐧 𝐀𝐈 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: 𝐒𝐞𝐥𝐟-𝐀𝐝𝐚𝐩𝐭𝐢𝐧𝐠 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐒𝐄𝐀𝐋) – 𝐓𝐨𝐰𝐚𝐫𝐝𝐬 𝐓𝐫𝐮𝐥𝐲 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐒𝐞𝐥𝐟-𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭

Recently published by MIT and the Improbable AI Lab, the study “Self-Adapting Language Models (SEAL)” represents a major paradigm shift in artificial intelligence. For the first time, a language model has been trained to autonomously improve itself by generating its own fine-tuning data and updating its own parameters.

🔍 What makes SEAL unique?

• Until now, LLMs such as GPT or Llama have been essentially static after training. They could be fine-tuned for specific tasks, but lacked the capacity to develop their own strategies for adaptation.

• SEAL fundamentally changes this: The model generates its own fine-tuning data and autonomously determines how to best adapt to new tasks or knowledge.

• This self-adaptation is implemented via a novel reinforcement learning loop: The model creates self-edit instructions, evaluates their impact, and directly rewards improvements – all without external supervision.

📈 Highlights of the results:

• On SQuAD (QA, knowledge integration), SEAL outperforms even synthetic data generated by GPT-4.1 after just two training iterations – and achieves this with a smaller base model.

• In few-shot learning scenarios, SEAL achieves a 72.5% success rate, compared to only 20% for classic approaches, illustrating the immense potential of self-directed model adaptation.

• Notably, SEAL operates independently of specific data formats – it can learn a variety of structures for self-training and weight adjustment.

🧠 Why is this revolutionary?
We are approaching a future in which language models will be able to autonomously ingest new information and embed it internally – without human guidance, relying entirely on their own data generation and selection. This is not only a significant step toward autonomous AI, but also provides an answer to the impending “data wall” in large-scale model training.

💡 My conclusion:
SEAL is more than an efficiency hack – it marks the beginning of the era of truly self-optimizing AI systems. This paradigm shift will have profound implications for research, industry, and ultimately the entire digital infrastructure.

👉 Link to the preprint (open access)
More information & code: https://jyopari.github.io/posts/seal

www.Trauth-Research.com

#AI #DeepLearning #ReinforcementLearning #MetaLearning #LLM #AutonomousAI