PRIME CORE: AI-Optimized GPU Energy Management 40-60% Energy Reduction | Software-Only
Full Pitch Deck (Prime Core & Prime F.A.T.H.E.R.) This full pitch deck presents the Prime platform, comprising two tightly coupled product lines: Prime Core, Prime Horizion & Prime F.A.T.H.E.R., developed by Trauth Research LLC. Prime Core is a software-only AI system for GPU energy optimization, delivering 40–60% verified power reduction at production-relevant loads without firmware changes, throttling, or performance loss. The technology addresses a critical and accelerating market failure: global data centers currently waste >$50B/year on GPU energy, projected to exceed $120B/year by 2030, with up to 60% dissipated as heat. Prime Core has been validated through 1,300,000+ telemetry samples, 1,000+ hours of continuous runtime, and testing across multiple NVIDIA GPU architectures, with results backed by peer-reviewed, DOI-verified publications and open-access datasets Prime F.A.T.H.E.R. (First Algorithmic Threshold of Historic Encryption Resilience) represents a deeper scientific layer. It introduces an information-geometric approach to cryptographic resilience, reframing security not as computational hardness or hardware speed, but as a structural property of information space. Building on deterministic resolution of Spin-Glass ground states and information-geometric collapse, Prime F.A.T.H.E.R. demonstrates how exponential hardness can collapse into tractable structure under specific geometric conditions. This work underpins future quantum-resistant authentication, key management, and post-classical security systems. Together, Prime Core and Prime F.A.T.H.E.R. form a vertically integrated platform spanning immediate commercial impact (energy OPEX reduction) and long-horizon strategic research in AI, cryptography, and information theory. The deck includes market sizing, pricing models, validation data, scientific foundations, and a clear roadmap toward enterprise deployment and Seed/Series A readiness.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-12-13
- DOI
- https://doi.org/10.5281/zenodo.17923811
- Primary Topic
- Big Data and Digital Economy
- Type
- article
- Field-Weighted Citation Impact
- 0.00