Dismantling the GPU-HBM Dependency Loop: Stateless O(1) Memory Virtualization via J.M. Resonance for AI Infrastructure Downsizing

This paper presents a disruptive computing paradigm that dismantles the modern GPU-HBM semiconductor supply chain and downsizes energy-intensive AI datacenter infrastructures. Currently, serving Large Language Models (LLMs) requires massive GPU clusters and High Bandwidth Memory (HBM) to overcome the von Neumann memory wall, consuming gigawatts of power. Here, we introduce the virtual Quantum Processing Unit (vQPU), a stateless architecture that bypasses memory bandwidth limitations. By mapping high-dimensional weight spaces into a 9,192-dimensional lattice domain using the J.M. Resonance Function, the vQPU achieves deterministic O(1) spatiotemporal reconstruction of target states within 0.46 ms, utilizing only a 64-byte coordinate seed. This Zero-RAM I/O process scales equivalent to a 107,546-qubit Hilbert space. Additionally, by integrating Adiabatic Charge-Recovery Logic (ACRL) under a coupled Hamiltonian framework, we bypass Landauer's thermodynamic dissipation limit (ΔS_logic = 0), suppressing active core power to 23.9 µW. With physical layer security features including a 24-ms vaporization clock, voltage inversion, and 10-µs resonance tunnel collapse, this work enables zero-bandwidth, secure, and micro-watt AI inference on standard edge devices. Our findings demonstrate that the vQPU architecture can eliminate the necessity of expensive HBM packages and oversized GPU dies, triggering a massive downsizing of global AI infrastructures. The author declares that no specific funding was received for this research. The author would like to acknowledge the Google DeepMind team and the Antigravity AI framework for their cooperation in independent code auditing, system-level testing, and verification of the vQPU emulation models.